Digital scholarship blog

13 posts categorized "Printed books"

24 January 2017

Publication of Quarterly Lists: Catalogues of Indian Books

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The Two Centuries of Indian Print project is pleased to announce the online availability of some wonderful catalogues held by the library, generally known as the Quarterly Lists. They record books published quarterly and by province of British India between 1867 and 1947.

Digitised for the first time, the Quarterly Lists can now be accessed as searchable PDFs via the British Library's datasets portal, Researchers will be able to examine rich bibliographic data about books published throughout India, including the names and address of printers and publishers, publication price and how many copies were sold.




Our next steps will be to OCR the Quarterly Lists to create ALTO XML for every page, which is designed to show accurate representations of the content layout. This will allow researchers to apply computational tools and methods to look across all of the lists to answer their questions about book history. So if a researcher is interested in what the history of book publishing reveals about a particular time period and place, we would like to make that possible by giving them full access to this dataset.

To get to this point however, we will have to overcome the layout challenge that the Quarterly Lists present. Across all of the lists we have found a few different layout styles which are rather tricky for OCR solutions to handle meaningfully. Note for instance how the list below compares to the one from the Calcutta Gazette above. Through the Digital Research strand of the project we will be seeking out innovative research groups willing to take a crack at improving the OCR quality and accuracy of tabular text extraction from the Quarterly Lists. 

The Quarterly Lists available on are out of copyright and openly licensed for reuse. If you or anyone you know are interested in using the Quarterly Lists in your research or simply want to find out more about them, feel free to drop me an email; or follow more about the project @BL_IndianPrint

You can read more about the history of the Quarterly Lists, in a previous blog I wrote last year.

10 November 2016

British Library Labs Symposium 2016 - Competition and Award Winners

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The 4th annual British Library Labs Symposium took place on 7th November 2016 and was a resounding success! 

More than 220 people attended and the event was a fantastic experience, showcasing and celebrating the Digital Scholarship field and highlighting the work of BL Labs and their collaborators. The Symposium included a number of exciting announcements about the winners of the BL Labs Competition and BL Labs Awards, who are presented in this blog post. Separate posts will be published about the runners up of the Competition and Awards and posts written by all of the winners and runners up about their work are also scheduled for the next few weeks - watch this space!

BL Labs Competition winner for 2016

Roly Keating, Chief Executive of the British Library announced that the overall winner of the BL Labs Competition for 2016 was...

SherlockNet: Using Convolutional Neural Networks to automatically tag and caption the British Library Flickr collection
By Karen Wang and Luda Zhao, Masters students at Stanford University, and Brian Do, Harvard Medicine MD student

Machine learning can extract information and insights from data on a massive scale. The project developed and optimised Convolutional Neural Networks (CNN), inspired by biological neural networks in the brain, in order to tag and caption the British Library’s Flickr Commons 1 million collection. In the first step of the project, images were classified with general categorical tags (e.g. “people”, “maps”). This served as the basis for the development of new ways to facilitate rapid online tagging with user-defined sets of tags. In the second stage, automatically generate descriptive natural-language captions were provided for images (e.g. “A man in a meadow on a horse”). This computationally guided approach has produced automatic pattern recognition which provides a more intuitive way for researchers to discover and use images. The tags and captions will be made accessible and searchable by the public through the web-based interface and text annotations will be used to globally analyse trends in the Flickr collection over time.

SherlockNet team presenting at the Symposium

Karen Wang is currently a senior studying Computer Science at Stanford University, California. She also has an Art Practice minor. Karen is very interested in the intersection of computer science and humanities research, so this project is near and dear to her heart! She will be continuing her studies next year at Stanford in CS, Artificial Intelligence track.

Luda Zhao is currently a Masters student studying Computer Science at Stanford University, living in Palo Alto, California. He is interested in using machine learning and data mining to tackle tough problems in a variety of real-life contexts, and he's excited to work with the British Library to make art more discoverable for people everywhere.

Brian Do grew up in sunny California and is a first-year MD/PhD student at Harvard Medical School. Previously he studied Computer Science and biology at Stanford. Brian loves using data visualisation and cutting edge tools to reveal unexpected things about sports, finance and even his own text message history.

SherlockNet recently posted an update of their work and you can try out their SherlockNet interface and tell us what you think.

BL Labs Awards winners for 2016

Research Award winner

Allan Sudlow, Head of Research Development at the British Library announced that the winner of the Research Award was...

Scissors and Paste

By Melodee Beals, Lecturer in Digital History at Loughborough University and historian of migration and media

Melodee Beals presenting Scissors & Paste

Scissors and Paste utilises the 1800-1900 digitised British Library Newspapers, collection to explore the possibilities of mining large-scale newspaper databases for reprinted and repurposed news content. The project has involved the development of a suite of tools and methodologies, created using both out-of-the-box and custom-made project-specific software, to efficiently identify reprint families of journalistic texts and then suggest both directionality and branching within these subsets. From these case-studies, detailed analyses of additions, omissions and wholesale changes offer insights into the mechanics of reprinting that left behind few if any other traces in the historical record.

Melodee Beals joined the Department of Politics, History and International Relations at Loughborough University in September 2015. Previously, Melodee has worked as a pedagogical researcher for the History Subject Centre, a teaching fellow for the School of Comparative American Studies at the University of Warwick and a Principal Lecturer for Sheffield Hallam University, where she acted as Subject Group Leader for History. Melodee completed her PhD at the University of Glasgow.

Commercial Award winner

Isabel Oswell, Head of Business Audiences at the British Library announced that the winner of the Commercial Award was...

Curating Digital Collections to Go Mobile

By Mitchel Davis, publishing and media entrepreneur

Mitchell Davis presenting Curating Digital Collections to Go Mobile

As a direct result of its collaborative work with the British Library, BiblioLabs has developed BiblioBoard, an award-winning e-Content delivery platform, and online curatorial and multimedia publishing tools to support it. These tools make it simple for subject area experts to create visually stunning multi-media exhibits for the web and mobile devices without any technical expertise. The curatorial output is almost instantly available via a fully responsive web site as well as through native apps for mobile devices. This unified digital library interface incorporates viewers for PDF, ePub, images, documents, video and audio files allowing users to immerse themselves in the content without having to link out to other sites to view disparate media formats.

Mitchell Davis founded BookSurge in 2000, the world’s first integrated global print-on-demand and publishing services company (sold to in 2005 and re-branded as CreateSpace). Since 2008, he has been founder and chief business officer of BiblioLabs- the creators of BiblioBoard. Mitchell is also an indie producer and publisher who has created several award winning indie books and documentary films over the past decade through Organic Process Productions, a small philanthropic media company he founded with his wife Farrah Hoffmire in 2005.

Artistic Award winner

Jamie Andrews, Head of Culture and Learning at the British Library announced that the winner of the Artistic Award was... 

Here there, Young Sailor

Written and directed by writer and filmmaker Ling Low and visual art by Lyn Ong

Hey There, Young Sailor combines live action with animation, hand-drawn artwork and found archive images to tell a love story set at sea. Inspired by the works of early cinema pioneer Georges Méliès, the video draws on late 19th century and early 20th century images from the British Library's Flickr collection for its collages and tableaux. The video was commissioned by Malaysian indie folk band The Impatient Sisters and independently produced by a Malaysian and Indonesian team.

Ling Low receives her Award from Jamie Andrews

Ling Low is based between Malaysia and the UK and she has written and directed various short films and music videos. In her fiction and films, Ling is drawn to the complexities of human relationships and missed connections. By day, she works as a journalist and media consultant. Ling has edited a non-fiction anthology of human interest journalism, entitled Stories From The City: Rediscovering Kuala Lumpur, published in 2016. Her journalism has also been published widely, including in the Guardian, the Telegraph and Esquire Malaysia.

Teaching / Learning Award winner

Ria Bartlett, Lead Producer: Onsite Learning at the British Library announced that the winner of the Teaching / Learning Award was...

Library Carpentry

Founded by James Baker, Lecturer at the Sussex Humanities Lab, who represented the global Library Carpentry Team (see below) at the Symposium

James Baker presenting Library Carpentry

Library Carpentry is software skills training aimed at the needs and requirements of library professionals. It takes the form of a series of modules that are available online for self-directed study or for adaption and reuse by library professionals in face-to-face workshops. Library Carpentry is in the commons and for the commons: it is not tied to any institution or person. For more information on Library Carpentry see

James Baker is a Lecturer in Digital History and Archives at the School of History, Art History and Philosophy and at the Sussex Humanities Lab. He is a historian of the long eighteenth century and contemporary Britain. James is a Software Sustainability Institute Fellow and holds degrees from the University of Southampton and latterly the University of Kent. Prior to joining Sussex, James has held positions of Digital Curator at the British Library and Postdoctoral Fellow with the Paul Mellon Centre for Studies of British Art. James is a convenor of the Institute of Historical Research Digital History seminar and a member of the History Lab Plus Advisory Board.

 The Library Carpentry Team is regularly accepting new members and currently also includes: 

The Library Carpentry Team

British Library Labs Staff Award winner

Phil Spence, Chief Operating Officer at the British Library announced that the winner of the British Library Labs Staff Award was...


Led by Alex Mendes, Software Developer at the British Library

LibCrowds is a crowdsourcing platform built by Alexander Mendes. It aims to create searchable catalogue records for some of the hundreds of thousands of items that can currently only be found in printed and card catalogues. By participating in the crowdsourcing projects, users will help researchers everywhere to access the British Library’s collections more easily in the future.

Nora McGregor presenting LibCrowds on behalf of Alex Mendes

The first project series, Convert-a-Card, experimented with a new method for transforming printed card catalogues into electronic records for inclusion in our online catalogue Explore, by asking volunteers to link scanned images of the cards with records retrieved from the WorldCat database. Additional projects have recently been launched that invite volunteers to transcribe cards that may require more specific language skills, such as the South Asian minor languages. Records matched, located, transcribed or translated as part of the crowdsourcing projects were uploaded to the British Library's Explore catalogue for anyone to search online. By participating users can have a direct impact on the availability of research material to anyone interested in the diverse collections available at the British Library.

Alex Mendes has worked at the British Library for several years and recently completed a Bachelor’s degree in Computer Science with the Open University. Alex enjoys the consistent challenges encountered when attempting to find innovative new solutions to unusual problems in software development.

Alex Mendes

If you would like to find out more about BL Labs, our Competition or Awards please contact us at   

03 November 2016

SherlockNet update - 10s of millions more tags and thousands of captions added to the BL Flickr Images!

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SherlockNet are Brian Do, Karen Wang and Luda Zhao, finalists for the Labs Competition 2016.

We have some exciting updates regarding SherlockNet, our ongoing efforts to using machine learning techniques to radically improve the discoverability of the British Library Flickr Commons image dataset.


Over the past two months we’ve been working on expanding and refining the set of tags assigned to each image. Initially, we set out simply to assign the images to one of 11 categories, which worked surprisingly well with less than a 20% error rate. But we realised that people usually search from a much larger set of words, and we spent a lot of time thinking about how we would assign more descriptive tags to each image.

Eventually, we settled on a Google Images style approach, where we parse the text surrounding each image and use it to get a relevant set of tags. Luckily, the British Library digitised the text around all 1 million images back in 2007-8 using Optical Character Recognition (OCR), so we were able to grab this data. We explored computational tools such as Term Frequency – Inverse Document Frequency (Tf-idf) and Latent Dirichlet allocation (LDA), which try to assign the most “informative” words to each image, but found that images aren’t always associated with the words on the page.

To solve this problem, we decided to use a 'voting' system where we find the 20 images most similar to our image of interest, and have all images vote on the nouns that appear most commonly in their surrounding text. The most commonly appearing words will be the tags we assign to the image. Despite some computational hurdles selecting the 20 most similar images from a set of 1 million, we were able to achieve this goal. Along the way, we encountered several interesting problems.

Similar images
For all images, similar images are displayed
  1. Spelling was a particularly difficult issue. The OCR algorithms that were state of the art back in 2007-2008 are now obsolete, so a sizable portion of our digitised text was misspelled / transcribed incorrectly. We used a pretty complicated decision tree to fix misspelled words. In a nutshell, it amounted to finding the word that a) is most common across British English literature and b) has the smallest edit distance relative to our misspelled word. Edit distance is the fewest number of edits (additions, deletions, substitutions) needed to transform one word into another.
  2. Words come in various forms (e.g. ‘interest’, ‘interested’, ‘interestingly’) and these forms have to be resolved into one “stem” (in this case, ‘interest’). Luckily, natural language toolkits have stemmers that do this for us. It doesn’t work all the time (e.g. ‘United States’ becomes ‘United St’ because ‘ates’ is a common suffix) but we can use various modes of spell-check trickery to fix these induced misspellings.
  3. About 5% of our books are in French, German, or Spanish. In this first iteration of the project we wanted to stick to English tags, so how do we detect if a word is English or not? We found that checking each misspelled (in English) word against all 3 foreign dictionaries would be extremely computationally intensive, so we decided to throw out all misspelled words for which the edit distance to the closest English word was greater than three. In other words, foreign words are very different from real English words, unlike misspelled words which are much closer.
  4. Several words appear very frequently in all 11 categories of images. These words were ‘great’, ‘time’, ‘large’, ‘part’, ‘good’, ‘small’, ‘long’, and ‘present’. We removed these words as they would be uninformative tags.

In the end, we ended up with between 10 and 20 tags for each image. We estimate that between 30% and 50% of the tags convey some information about the image, and the other ones are circumstantial. Even at this stage, it has been immensely helpful in some of the searches we’ve done already (check out “bird”, “dog”, “mine”, “circle”, and “arch” as examples). We are actively looking for suggestions to improve our tagging accuracy. Nevertheless, we’re extremely excited that images now have useful annotations attached to them!

SherlockNet Interface

SherlockNet Interface

For the past few weeks we’ve been working on the incorporation of ~20 million tags and related images and uploading them onto our website. Luckily, Amazon Web Services provides comprehensive computing resources to take care of storing and transferring our data into databases to be queried by the front-end.

In order to make searching easier we’ve also added functionality to automatically include synonyms in your search. For example, you can type in “lady”, click on Synonym Search, and it adds “gentlewoman”, “ma'am”, “madam”, “noblewoman”, and “peeress” to your search as well. This is particularly useful in a tag-based indexing approach as we are using.

As our data gets uploaded over the coming days, you should begin to see our generated tags and related images show up on the Flickr website. You can click on each image to view it in more detail, or on each tag to re-query the website for that particular tag. This way users can easily browse relevant images or tags to find what they are interested in.

Each image is currently captioned with a default description containing information on which source the image came from. As Luda finishes up his captioning, we will begin uploading his captions as well.

We will also be working on adding more advanced search capabilities via wrapper calls to the Flickr API. Proposed functionality will include logical AND and NOT operators, as well as better filtering by machine tags.


As mentioned in our previous post, we have been experimenting with techniques to automatically caption images with relevant natural language captions. Since an Artificial Intelligence (AI) is responsible for recognising, understanding, and learning proper language models for captions, we expected the task to be far harder than that of tagging, and although the final results we obtained may not be ready for a production-level archival purposes, we hope our work can help spark further research in this field.

Our last post left off with our usage of a pre-trained Convolutional Neural Networks - Recurrent Neural Networks (CNN-RNN) architecture to caption images. We showed that we were able to produce some interesting captions, albeit at low accuracy. The problem we pinpointed was in the training set of the model, which was derived from the Microsoft COCO dataset, consisting of photographs of modern day scenes, which differs significantly from the BL Flickr dataset.

Through collaboration with BL Labs, we were able to locate a dataset that was potentially better for our purposes: the British Museum prints and drawing online collection, consisting of over 200,000 print drawing, and illustrations, along with handwritten captions describing the image, which the British Museum has generously given us permission to use in this context. However, since the dataset is directly obtained from the public SPARQL endpoints, we needed to run some pre-processing to make it usable. For the images, we cropped them to standard 225 x 225 size and converted them to grayscale. For caption, pre-processing ranged from simple exclusion of dates and author information, to more sophisticated “normalization” procedures, aimed to lessen the size of the total vocabulary of the captions. For words that are exceeding rare (<8 occurrences), we replaced them with <UNK> (unknown) symbols denoting their rarity. We used the same neuraltalk architecture, using the features from a Very Deep Convolutional Networks for Large-Scale Visual Recognition (VGGNet) as intermediate input into the language model. As it turns out, even with aggressive filtering of words, the distribution of vocabulary in this dataset was still too diverse for the model. Despite our best efforts to tune hyperparameters, the model we trained was consistently over-sensitive to key phrases in the dataset, which results in the model converging on local minimums where the captions would stay the same and not show any variation. This seems to be a hard barrier to learning from this dataset. We will be publishing our code in the future, and we welcome anyone with any insight to continue on this research.

Although there were occasion images with delightfully detailed captions (left), our models couldn’t quite capture useful information for the vast majority of the images(right). More work is definitely needed in this area!

The British Museum dataset (Prints and Drawings from the 19th Century) however, does contain valuable contextual data, and due to our difficulty in using it to directly caption the dataset, we decided to use it in other ways. By parsing the caption and performing Part-Of-Speech (POS) tagging, we were able to extract nouns and proper nouns from each caption. We then compiled common nouns from all the images and filtered out the most common(>=500 images) as tags, resulting in over 1100 different tags. This essentially converts the British Museum dataset into a rich dataset of diverse tags, which we would be able to apply to our earlier work with tag classification. We trained a few models with some “fun” tags, such as “Napoleon”, “parrots” and “angels”, and we were able to get decent testing accuracies of over 75% on binary labels. We will be uploading a subset of these tags under the “sherlocknet:tags” prefix to the Flickr image set, as well as the previous COCO captions for a small subset of images(~100K).

You can access our interface here: or look for 'sherlocknet:tag=' and 'sherlocknet:category=' tags on the British Library Flickr Commons site, here is an example, and see the image below:

Sherlocknet tags
Example Tags on a Flickr Image generated by SherlockNet

Please check it out and let us know if you have any feedback!

We are really excited that we will be there in London in a few days time to present our findings, why don't you come and join us at the British Library Labs Symposium, between 0930 - 1730 on Monday 7th of November, 2016?

Black Abolitionist Performances and their Presence in Britain - An update!

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Posted by Hannah-Rose Murray, finalist in the BL Labs Competition 2016.

Reflecting back on an incredible and interesting journey over the last few months, it is remarkable at the speed in which five months has flown by! In May, I was chosen as one of the finalists for the British Library Labs Competition 2016, and my project has focused on black abolitionist performances and their presence in Britain during the nineteenth century. Black men and women had an impact in nearly every part of Great Britain, and it is of no surprise to learn their lectures were held in famous meeting halls, taverns, the houses of wealthy patrons, theatres, and churches across the country: we inevitably and unknowably walk past sites with a rich history of Black Britain every day.

I was inspired to apply for this competition by last year’s winner, Katrina Navickas. Her project focused on the Chartist movement, and in particular using the nineteenth century digitised newspaper database to find locations of Chartist meetings around the country. Katrina and the Labs team wrote code to identify these meetings in the Chartist newspaper, and churned out hundreds of results that would have taken her years to search manually.

I wanted to do the same thing, but with black abolitionist speeches. However, there was an inherent problem: these abolitionists travelled to Britain between 1830-1900 and gave lectures in large cities and small towns: in other words their lectures were covered in numerous city and provincial newspapers. The scale of the project was perhaps one of the most difficult things we have had to deal with.

When searching the newspapers, one of the first things we found was the OCR (Optical Character Recognition) is patchy at best. OCR refers to scanned images that have been turned into machine-readable text, and the quality of the OCR depended on many factors – from the quality of the scan itself, to the quality of the paper the newspaper was printed on, to whether it has been damaged or ‘muddied.’ If the OCR is unintelligible, the data will not be ‘read’ properly – hence there could be hundreds of references to Frederick Douglass that are not accessible or ‘readable’ to us through an electronic search (see the image below).

An excerpt from a newspaper article about a public meeting about slavery, from the Leamington Spa Courier, 20 February 1847

In order to 'clean' and sort through the ‘muddied’ OCR and the ‘clean’ OCR, we need to teach the computer what is ‘positive text’ (i.e., language that uses the word ‘abolitionist’, ‘black’, ‘fugitive’, ‘negro’) and ‘negative text’ (language that does not relate to abolition). For example, the image to the left shows an advert for one of Frederick Douglass’s lectures (Leamington Spa Courier, 20 February 1847). The key words in this particular advert that are likely to appear in other adverts, reports and commentaries are ‘Frederick Douglass’, ‘fugitive’, ‘slave’, ‘American’, and ‘slavery.’ I can search for this advert through the digitised database, but there are perhaps hundreds more waiting to be uncovered.
We found examples where the name ‘Frederick’ had been ‘read’ as F!e83hrick or something similar. The image below shows some OCR from the Aberdeen Journal, 5 February 1851, and an article about “three fugitive slaves.” The term ‘Fugitive Slaves’ as a heading is completely illegible, as is William’s name before ‘Crafts.’ If I used a search engine to search for William Craft, it is unlikely this result would be highlighted because of the poor OCR.

OCR from the Aberdeen Journal, 5 February 1851, and an article about “three fugitive slaves.”

I have spent several years transcribing black abolitionist speeches and most of this will act as the ‘positive’ text. ‘Negative’ text can refer to other lectures of a similar structure but do not relate to abolition specifically, for example prison reform meetings or meetings about church finances. This will ensure the abolitionist language becomes easily readable. We can then test the performance of this against some of the data we already have, and once the probability ensures we are on the right track, we can apply it to a larger data set.

All of this data is built into what is called a classifier, created by Ben O’Steen, Technical Lead of BL Labs. This classifier will read the OCR and collect newspaper references, but works differently to a search engine because it measures words by weight and frequency. It also relies on probability, so for example, if there is an article that mentions fugitive and slave in the same section, it ranks a higher probability that article will be discussing someone like Frederick Douglass or William Craft. On the other hand, a search engine might read the word ‘fugitive slave’ in different articles on the same page of a newspaper.

We’re currently processing the results of the classifier, and adjusting accordingly to try and reach a higher accuracy. This involves some degree of human effort while I double check the references to see whether the results actually contains an abolitionist speech. So far, we have had a few references to abolitionist speeches, but the classifier’s biggest difficulty is language. For example, there were hundreds of results from the 1830s and the 1860s – I instantly knew that these would be references around the Chartist movement because the language the Chartists used would include words like ‘slavery’ when describing labour conditions, and frequently compared these conditions to ‘negro slavery’ in the US. The large number of references from the 1860s highlight the renewed interest in American slavery because of the American Civil War, and there are thousands of articles discussing the Union, Confederacy, slavery and the position of black people as fugitives or soldiers. Several times, the results focused on fugitive slaves in America and not in Britain.

Another result we had referred to a West Indian lion tamer in London! This is a fascinating story and part of the hidden history we see as a central part of the project, but is obviously not an abolitionist speech. We are currently working on restricting our date parameters from 1845 to 1860 to start with, to avoid numerous mentions of Chartists and the War. This is one way in which we have had to be flexible with the initial proposal of the project.

Aside from the work on the classifier, we have also been working on numerous ways to improve the OCR – is it better to apply OCR correction software or is it more beneficial to completely re-OCR the collection, or perhaps a combination of both? We have sent some small samples to a company based in Canberra, Australia called Overproof, who specialise in OCR correction and have provided promising results. Obviously the results are on a small scale but it’s been really interesting so far to see the improvements in today’s software compared to when some of these newspapers were originally scanned ten years before. We have also sent the same sample to the IMPACT centre for competence of Competence in Digitisation whose mission is to make the digitisation of historical printed text “better, faster, cheaper” and provides tools, services and facilities to further advance the state-of-the-art in the field of document imaging, language technology and the processing of historical text. Preliminary results will be presented at the Labs Symposium.

Updated website

Before I started working with the Library, I had designed a website at The structure was rudimentary and slightly awkward, dwarfed by the numerous pages I kept adding to it. As the project progressed, I wanted to improve the website at the same time, and with the invaluable help of Dr Mike Gardner from the University of Nottingham, I re-launched my website at the end of October. Initially, I had two maps, one showing the speaking locations of Frederick Douglass, and another map showing speaking locations by other black abolitionists such as William and Ellen Craft, William Wells Brown and Moses Roper (shown below).

Left map showing the speaking locations of Frederick Douglass. Right map showing speaking locations by other black abolitionists such as William and Ellen Craft, William Wells Brown and Moses Roper.

After working with Mike, we not only improved the aesthetics of the website and the maps (making them more professional) but we also used clustering to highlight the areas where these men and women spoke the most. This avoided the ‘busy’ appearance of the first maps and allowed visitors to explore individual places and lectures more efficiently, as the old maps had one pin per location. Furthermore, on the black abolitionist speaking locations map (below right), a user can choose an individual and see only their lectures, or choose two or three in order to correlate patterns between who gave these lectures and where they travelled. 

The new map interface for my website.


I am very passionate about public engagement and regard it as an essential part of being an academic, since it is so important to engage and share with, and learn from, the public. We have created two events: as part of Black History Month on the 6th October, we had a performance here at the Library celebrating the life of two formerly enslaved individuals named William and Ellen Craft. Joe Williams of Heritage Corner in Leeds – an actor and researcher who has performed as numerous people such as Frederick Douglass and the black circus entertainer Pablo Fanque – had been writing a play about the Crafts, and because it fitted so well with the project, we invited Joe and actress Martelle Edinborough, who played Ellen, to London for a performance. Both Joe and Martelle were incredible and it really brought the Craft’s story and the project to life. We had a Q&A afterwards where everyone was very responsive and positive to the performance and the Craft’s story of heroism and bravery.

(Left to Right) Martelle Edinborough, Hannah-Rose Murray and Joe Williams

The next event is a walking tour, taking place on Saturday 26 November. I’ve devised this tour around central London, highlighting six sites where black activists made an indelible mark on British society during the nineteenth century. It is a way of showing how we walk past these sites on a daily basis, and how we need to recognise the contributions of these individuals to British history.

Hopefully this project will inspire others to research and use digital scholarship to find more ‘hidden voices’ in the archive. In terms of black history specifically, people of colour were actors, sailors, boxers, students, authors as well as lecturers, and there is so much more to uncover about their contribution to British history. My personal journey with the Library and the Labs team has also been a rewarding experience. It has further convinced me that we need stronger networks of collaboration between scholars and computer scientists, and the value of digital humanities in general. Academics could harness the power of technology to bring their research to life, an important and necessary tool for public engagement. I hope to continue working with the Labs team fine-tuning some of the results, as well as writing some pages about black abolitionists for the new website. I’m very grateful to the Library and the Labs team for their support, patience, and this amazing opportunity as I’ve learned so much about digital humanities, and this project – with its combination of manual and technological methods – as a larger model for how we should move forward in the future. The project will shape my career in new and exciting ways, and the opportunity to work with one of the best libraries in the world is a really gratifying experience.

I am really excited that I will be there in London in a few days time to present my findings, why don't you come and join us at the British Library Labs Symposium, between 0930 - 1730 on Monday 7th of November, 2016?

02 November 2016

An Overview of 6 Second History Animated British Library Photographs & Illustrations

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Posted by Mahendra Mahey on behalf of Nick Cave, entrant in the BL Labs Awards 2016 under the 'Artistic Category'. 

Nick Cave - Animator

Today’s blockbuster films sees long forgotten dinosaurs, futuristic warping spaceships and metallic looking beings the size of our tallest buildings, transforming from a car to a giant robot in the blink of an eye. There are even whole planets of giant alien creatures walking amongst people and trees and all of these incredible visual showcases are invading our cinema screens week in, week out.

However, back before the advent of sophisticated computer generated graphics technology, artists were using simpler photographic techniques to create short animations with zany characters, new landscapes and everyday objects to bring laughter to the masses on the small screen. One such artist was Terry Gilliam of Monty Python fame who used a technique known as stop motion animation to bring a variety of household magazine pictures to life. By cutting out different pictures, photographing and filming them individually moving over time, they became very funny animated cartoon like sketches.

Terry Gilliam - Monty Python animations

I’ve always been fascinated by this amazing technique, even if it is a very time consuming one, but modern computer animation software makes this process easier. Stop motion animations simply have a quirky charm. More often than not they’re animations which are not polished, or even necessarily slick looking. It’s a style of animation which creates imaginary worlds and characters, often moving in a jagged, staccato fashion, but still somehow one that looks and feels as engaging and interesting as modern visual effects which have cost millions to create. So, with the Terry Gilliam magazine picture ideas in mind where to start, social media of course! 

An example of working on stop motion animation

Social media is dominated by celebrity gossip and tittle tattle, breaking news, but major events also continue to play a key part in posts. This could be when celebrating sporting achievements, raving about a new film, TV show, or even an anniversary event and significantly, nostalgia.

People always like to look back and remember, which is where my 6SecondHistory idea spawned from. I chose Instagram as my social media delivery platform, partly because mini web episodes, such as crime thriller, Shield5, had been very successful on it and partly because it’s a social media platform created specifically to showcase photographs and short videos.

As copyright can be a contentious legal minefield, where to source the modern equivalent of historical magazine photos from? Well, easy, the British Library has a massive collection of freely available copyright free Flickr archive photographs and illustrations to choose from. Animals, places, people, fancy illustrations from old manuscripts, basically a wealth of interesting material. The interesting and sometimes vexing challenge in bringing these pictures to life are many, because they’re often hand drawn with no clear differentiation between foreground and background objects, plus searching for specific pictures can sometimes bring up an interesting results set. Six second animations seemed a good starting point because of the success of internet vines, also six second gifs, or videos etc.

Images taken from the British Library Flickr Commons Site

Left - British Library Flickr Shakespeare Character ( Image taken from page 252 of 'The Oxford Thackeray. With illustrations. [Edited with introductions by George Saintsbury.]’

Top Right - British Library Flickr Skull ( Image taken from page 246 of 'Modern Science in Bible Lands ... With maps and illustrations’ - 1888

Bottom Right - British Library Flickr Shakespeare Theatre ( Image taken from page 76 of 'The Works of Shakspeare; from the text of Johnson, Steevens, and Reed. With a biographical memoir, and a variety of interesting matter, illustrative of his life and writings. By W. Harvey’ - 1825

As an example, 2016 saw the 400th anniversary of Shakespeare’s death.  Whenever the focus is on Shakespeare famous speeches are cited and one such speech is Hamlet’s Act 5 Scene 1 lament to a disembodied skull. Perfect material for a funny 6SecondHistory animation and one that could truly show off the merging of a variety of British Library archive pics, repurposed and coloured to create a comical short Hamlet animation with an element of 3D perspective in it. This was a labour of love, but I hope you agree that my short animation has brought the speech to life.

Here is a link to my animation:


See more of my work at: and

You can meet me, Nick Cave at the British Library Labs Symposium on Monday 7th of November 2016 at the British Library in London (we still have a few tickets left so book now).


31 October 2016

Datamining for verse in eighteenth-century newspapers - British Library Labs Project

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Posted by Mahendra Mahey on behalf of Jennifer Batt, second runner up in the British Library Labs Competition 2016.

Jennifer will be working with the BL Labs team between November 2016 and March 2017 on her project 'Datamining for verse in eighteenth-century newspapers' which is described below:

Datamining for verse in eighteenth-century newspapers
by Jennifer Batt, Lecturer in English at the University of Bristol

This project is designed to interrogate the digitised eighteenth-century newspapers in the British Library’s Burney Collection and British Newspaper Archive databases in order to recover a complex, expansive, ephemeral poetic culture that has been lost to us for well over 250 years.

In the eighteenth century, thousands of poems appeared in the newspapers that were printed the length and breadth of the country. Poems in newspapers were extraordinarily varied: some were light and inconsequential pieces designed to provide momentary diversion and elicit a smile or a raised eyebrow; others were topically-engaged works commenting on contemporary cultural or political events; and still others were literary verses in a range of different genres.

Swift LEP 7-9 nov 34
Jonathan Swift, 'On his own Deafness', in the London Evening Post, 7-9 November 1734, issue 1088.

Some of these poems were the work of established and professional writers; some were composed by amateur contributors; and others still were by countless anonymous individuals. Though much of this verse disappeared into obscurity after appearing in a single newspaper issue, a number of poems that began their printed lives in newspapers achieved a far wider dissemination, being copied from one paper into another and another (going viral, we might say) before making their way into magazines, miscellanies, songbooks, and manuscripts.

The rich, dynamic, ephemeral and responsive poetic culture that found a home in eighteenth-century newspapers has long been overlooked by literary scholars and cultural, not least because attempting to recover and map its extent – whether by flipping through the pages of physical copies of newspapers, scrolling through reproductions on microfilm, or pushing keyword searches into the Burney Collection or the British Newspaper Archive – is a time-consuming and often inefficient process.

Ingram old whig 16 dec
Anne Ingram, Viscountess Irwin, 'An Epistle to Mr. Pope', in The Old Whig or the Consistent Protestant, 16 December 1736, issue 93.

This project is an experiment designed to discover whether digital techniques – particularly data-mining and visualization – can be used to effectively and efficiently uncover the contours of this lost literary culture.

The BL Labs team have unrivalled experience in developing strategies to retrieve information of varying sorts – including Victorian jokes, information about political meetings, and patterns of reuse and plagiarism – from databases of historical newspapers. This project turns their expertise towards poetry, and asks, how far is it possible to use digital tools to effectively uncover and map the poetic culture that existed in eighteenth-century newspapers? By looking at both national and regional newspapers, is it possible to discover if there is a single, nationwide newspaper-based poetic culture, or whether there are regional variations? And how might the verse we can recover from newspapers enhance – or even challenge – our understanding of how people in the eighteenth century wrote, read, and thought about verse?

If you would like to meet Jennifer, she will be at the FREE British Library Labs Symposium (there are just a few tickets still available so book now to avoid disappointment) on Monday 7th of November 2016, at the British Library in London to talk about her work.

About the researcher:

Jennifer batt
Jennifer Batt, Lecturer in English at the University of Bristol.

Jennifer Batt is a Lecturer in English at the University of Bristol; her research focuses on eighteenth-century poetry, with a particular interest in the ways that verse is printed and reprinted across a range of different media. From 2010 to 2013, she was project manager and editor of the Digital Miscellanies Index ( based at the University of Oxford. 

22 August 2016

SherlockNet: tagging and captioning the British Library’s Flickr images

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Finalists of the BL Labs Competition 2016, Karen Wang, Luda Zhao and Brian Do, inform us on the progress of their SherlockNet project:


This is an update on SherlockNet, our project to use machine learning and other computational techniques to dramatically increase the discoverability of the British Library’s Flickr images dataset. Below is some of our progress on tagging, captioning, and the web interface.


When we started this project, our goal was to classify every single image in the British Library's Flickr collection into one of 12 tags -- animals, people, decorations, miniatures, landscapes, nature, architecture, objects, diagrams, text, seals, and maps. Over the course of our work, we realised the following:

  1. We were achieving incredible accuracy (>80%) in our classification using our computational methods.
  2. If our algorithm assigned two tags to an image with approximately equal probability, there was a high chance the image had elements associated with both tags.
  3. However, these tags were in no way enough to expose all the information in the images.
  4. Luckily, each image is associated with text on the corresponding page.

We thus wondered whether we could use the surrounding text of each image to help expand the “universe” of possible tags. While the text around an image may or may not be directly related to the image, this strategy isn’t without precedent: Google Images uses text as its main method of annotating images! So we decided to dig in and see how this would go.

As a first step, we took all digitised text from the three pages surrounding each image (the page before, the page of, and the page after) and extracted all noun phrases. We figured that although important information may be captured in verbs and adjectives, the main things people will be searching for are nouns. Besides, at this point this is a proof of principle that we can easily extend later to a larger set of words. We then constructed a composite set of all words from all images, and only kept words present in between 5% and 80% of documents. This was to get rid of words that were too rare (often misspellings) or too common (words like ‘the’, ‘a’, ‘me’ -- called “stop words” in the natural language processing field).

With this data we were able to use a tool called Latent Dirichlet Allocation (LDA) to find “clusters” of images in an automatic way. We chose the original 12 tags after manually going through 1,000 images on our own and deciding which categories made the most sense based on what we saw; but what if there are categories we overlooked or were unable to discern by hand? LDA solves this by trying to find a minimal set of tags where each document is represented by a set of tags, and each tag is represented by a set of words. Obviously the algorithm can’t provide meaning to each tag, so we provide meaning to the tag by looking at the words that are present or absent in each tag. We ran LDA on a sample of 10,000 images and found tags clusters for men, women, nature, and animals. Not coincidentally, these are similar to our original tags and represent a majority of our images.

This doesn’t solve our quest for expanding our tag universe though. One strategy we thought about was to just use the set of words from each page as the tags for each image. We quickly found, however, that most of the words around each image are irrelevant to the image, and in fact sometimes there was no relation at all. To solve this problem, we used a voting system [1]. From our computational algorithm, we found the 20 images most similar to the image in question. We then looked for the words that were found most often in the pages around these 20 images. We then use these words to describe the image in question. This actually works quite well in practice! We’re now trying to combine this strategy (finding generalised tags for images) with the simpler strategy (unique words that describe images) to come up with tags that describe images at different “levels”.

Image Captioning

We started with a very ambitious goal: given only the image input, can we give a machine -generated, natural-language description of the image with a reasonably high degree of accuracy and usefulness? Given the difficulty of the task and of our timeframe, we didn’t expect to get perfect results, but we’ve hoped to come up with a first prototype to demonstrate some of the recent advances and techniques that we hope will be promising for research and application in the future.

We planned to look at two approaches to this problem:

  • Similarity-based captioning. Images that are very similar to each other using a distance metric often share common objects, traits, and attributes that shows up in the distribution of words in their captions. By pooling words together from a bag of captions of similar images, one can come up with a reasonable caption for the target image.
  • Learning-based captioning. By utilising a CNN similar to what we used for tagging, we can capture higher-level features in images. We then attempt to learn the mappings between the higher-level features and their representations in words, using either another neural network or other methods.

We have made some promising forays into the second technique. As a first step, we used a pre-trained CNN-RNN architecture called NeuralTalk to caption our images. As the models are trained on the Microsoft COCO dataset, which consists of pictures and photograph that differs significantly from the British Library's  Flickr dataset, we expect the transfer of knowledge to be difficult. Indeed, the resulting captions of some ~1000 test images show that weakness, with the black-and-white exclusivity of the British Library illustration and the more abstract nature of some illustrations being major roadblocks in the qualities of the captioning. Many of the caption would comment on the “black and white” quality of the photo or “hallucinate” objects that did not exist in the images. However, there were some promising results that came back from the model. Below are some hand-pick examples. Note that this was generated with no other metadata; only the raw image was given.

S1 S2 S3
From a rough manual pass, we estimate that around 1 in 4 captions are of useable quality: accurate, contains interesting and useful data that would aid in search discovery, catalogisation etc., with occasional gems (like the elephant caption!). More work will be directed to help us increase this metric.

Web Interface

We have been working on building the web interface to expose this novel tag data to users around the world.

One thing that’s awesome about making the British Library dataset available via Flickr, is that Flickr provide an amazing API for developers. The API exposes, among other functions, the image website’s search logic via tags as well as free text search using the image title and description, and the capability to sort by a number of factors including relevance and “interestingness”. We’ve been working on using the Flickr API, along with AngularJS and Node.js to build a wireframe site. You can check it out here.

If you look at the demo or the British Library's Flickr album, you’ll see that each image has a relatively sparse set of tags to query from. Thus, our next steps will be adding our own tags and captions to each image on Flickr. We will pre-pend these with a custom namespace to distinguish them from existing user-contributed and machine tags, and utilise them in queries to find better results.

Finally, we are interested in what users will use the site for. For example, we could track user’s queries and which images they click on or save. These images are presumably more relevant to these queries, and we rank them higher in future queries. We also want to be able to track general analytics like the most popular queries over time. Thus incorporating user analytics will be the final step in building the web interface.

We welcome any feedback and questions you may have! Contact us at


[1] Johnson J, Ballan L, Fei-Fei L. Love Thy Neighbors: Image Annotation by Exploiting Image Metadata. arXiv (2016)

04 July 2016

Two Centuries of Indian Print: Enhancing Scholarly Research

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Tom Derrick will be working as a Digital Curator within the Digital Research Team at the British Library on a project titled ‘Two Centuries of Indian Print’. This project will digitise rare Bengali printed books and provide opportunities for innovative research at the intersection of Digital Humanities and South Asian studies. He Tweets @tommyid83, and can also be contacted by email at


Only a week into my new role I can already see the benefits of the work that the digital research team delivers. I attended a fascinating presentation of the two latest BL Lab award-winning projects. I was impressed to see how young researchers are collaborating with the digital research team here to find innovative methods to open up new avenues for their own research as well as for other academics and the general public.      

I have joined the British Library from a digital publisher of historical primary sources and am excited to use my experience engaging with researchers to facilitate academic interrogation of the Two Centuries of Indian Print project data. This two-year pilot will make, freely available online, digitised Bengali books drawn from the extensive South Asian printed book collection at the British Library along with a selection from SOAS. The books digitised as part of the pilot will span 1801-1867, the bulk of which are religious tracts. It is part of a wider initiative by the British Library to catalogue and make available printed Indian books in 22 South Asian languages, covering 1714-1914.

 Ab_Haval  Ab haval, a poetical account in Gujarati on the disastrous floods at Ahmadabad, 1875


Over the course of the next two years, I'll be engaging with researchers, particularly in the fields of South Asian studies and Digital Humanities, to explore the opportunities and challenges involved in applying digital research methods and tools to this newly digitised collection. A key area I'll be looking at is how to ensure the metadata and digitised text produced will cater to the needs and interests of an academic community interested in performing large-scale data analysis. This will involve finding an optimal solution to making the Bengali script machine readable so the full text can be searched and ‘mined’ by researchers. We'll also be developing a series of workshops to provide academics and professionals from Indian institutions, particularly the GLAM (Galleries, Libraries, Archives and Museums) sector, to gain new skills to support digital research.  

Sanskrit_Hymn_2 Illustration from an early printed edition of the Adityahṛdayam, a devotional hymn in Sanskrit to the Sun God, seen here on his chariot drawn by seven horses, Bombay, 1862


It is a privilege to be here working for the British Library, an institution I have always admired for its mission and core values and I am proud to support that continued effort through stimulating an international community of researchers to access what will prove to be a fascinating collection. We’ll be posting further blogs describing the progress of the project, so watch this space! If you have any questions about the project or ideas relating to innovative use of the collection, please do email me at