Wellcome Library Transcribing Recipes Project: Final Report

The Wellcome Library, in considering a project to digitise and transcribe recipe manuscripts using crowdsourcing technologies, commissioned this report from Ben Brumfield and Mia Ridge in Summer 2015. The report addresses issues specific to this project, and to the Wellcome Library's digital infrastructure

Toward the Optimized Crowdsourcing Strategy for OCR Post-Correction

Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using Optical Character Recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. This study investigates how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives. A series of experiments with different micro-task's structures and text lengths was conducted with 753 workers on the Amazon's Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures have been devised. The analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two-phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image. The study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction. This is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.Comment: 25 pages, 12 figures, 1 tabl

Transforming scholarship in the archives through handwritten text recognition:Transkribus as a case study

Purpose: An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. - Design/methodology/approach: This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. - Findings: Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. - Research limitations/implications: The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. - Practical implications: Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. - Social implications: The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. - Originality/value: This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector

Optimizing digital archiving: An artificial intelligence approach for OCR error correction

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis thesis research scopes the knowledge gap for effective ways to address OCR errors and the importance to have training datasets adequated size and quality, to promote digital documents OCR recognition efficiency. The main goal is to examine the effects regarding the following dimensions of sourcing data: input size vs performance vs time efficiency, and to propose a new design that includes a machine translation model, to automate the errors correction caused by OCR scan. The study implemented various LSTM, with different thresholds, to recover errors generated by OCR systems. However, the results did not overcomed the performance of existing OCR systems, due to dataset size limitations, a step further was achieved. A relationship between performance and input size was established, providing meaningful insights for future digital archiving systems optimisation. This dissertation creates a new approach, to deal with OCR problems and implementation considerations, that can be further followed, to optimise digital archive systems efficiency and results

Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence

The increasing capacities of large language models (LLMs) present an unprecedented opportunity to scale up data analytics in the humanities and social sciences, augmenting and automating qualitative analytic tasks previously typically allocated to human labor. This contribution proposes a systematic mixed methods framework to harness qualitative analytic expertise, machine scalability, and rigorous quantification, with attention to transparency and replicability. 16 machine-assisted case studies are showcased as proof of concept. Tasks include linguistic and discourse analysis, lexical semantic change detection, interview analysis, historical event cause inference and text mining, detection of political stance, text and idea reuse, genre composition in literature and film; social network inference, automated lexicography, missing metadata augmentation, and multimodal visual cultural analytics. In contrast to the focus on English in the emerging LLM applicability literature, many examples here deal with scenarios involving smaller languages and historical texts prone to digitization distortions. In all but the most difficult tasks requiring expert knowledge, generative LLMs can demonstrably serve as viable research instruments. LLM (and human) annotations may contain errors and variation, but the agreement rate can and should be accounted for in subsequent statistical modeling; a bootstrapping approach is discussed. The replications among the case studies illustrate how tasks previously requiring potentially months of team effort and complex computational pipelines, can now be accomplished by an LLM-assisted scholar in a fraction of the time. Importantly, this approach is not intended to replace, but to augment researcher knowledge and skills. With these opportunities in sight, qualitative expertise and the ability to pose insightful questions have arguably never been more critical

Transcribing a 17th-century botanical manuscript: Longitudinal evaluation of document layout detection and interactive transcription

[EN] We present a process for cost-effective transcription of cursive handwritten text images that has been tested on a 1,000-page 17th-century book about botanical species. The process comprised two main tasks, namely: (1) preprocessing: page layout analysis, text line detection, and extraction; and (2) transcription of the extracted text line images. Both tasks were carried out with semiautomatic pro- cedures, aimed at incrementally minimizing user correction effort, by means of computer-assisted line detection and interactive handwritten text recognition technologies. The contribution derived from this work is three-fold. First, we provide a detailed human-supervised transcription of a relatively large historical handwritten book, ready to be searchable, indexable, and accessible to cultural heritage scholars as well as the general public. Second, we have conducted the first longitudinal study to date on interactive handwriting text recognition, for which we provide a very comprehensive user assessment of the real-world per- formance of the technologies involved in this work. Third, as a result of this process, we have produced a detailed transcription and document layout infor- mation (i.e. high-quality labeled data) ready to be used by researchers working on automated technologies for document analysis and recognition.This work is supported by the European Commission through the EU projects HIMANIS (JPICH program, Spanish, grant Ref. PCIN-2015-068) and READ (Horizon-2020 program, grant Ref. 674943); and the Universitat Politecnica de Valencia (grant number SP20130189). This work was also part of the Valorization and I+D+i Resources program of VLC/CAMPUS and has been funded by the Spanish MECD as part of the International Excellence Campus program.Toselli, AH.; Leiva, LA.; Bordes-Cabrera, I.; Hernández-Tornero, C.; Bosch Campos, V.; Vidal, E. (2018). Transcribing a 17th-century botanical manuscript: Longitudinal evaluation of document layout detection and interactive transcription. Digital Scholarship in the Humanities. 33(1):173-202. https://doi.org/10.1093/llc/fqw064S173202331Bazzi, I., Schwartz, R., & Makhoul, J. (1999). An omnifont open-vocabulary OCR system for English and Arabic. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(6), 495-504. doi:10.1109/34.771314Causer, T., Tonra, J., & Wallace, V. (2012). Transcription maximized; expense minimized? Crowdsourcing and editing The Collected Works of Jeremy Bentham*. Literary and Linguistic Computing, 27(2), 119-137. doi:10.1093/llc/fqs004Ramel, J. Y., Leriche, S., Demonet, M. L., & Busson, S. (2007). User-driven page layout analysis of historical printed books. International Journal of Document Analysis and Recognition (IJDAR), 9(2-4), 243-261. doi:10.1007/s10032-007-0040-6Romero, V., Fornés, A., Serrano, N., Sánchez, J. A., Toselli, A. H., Frinken, V., … Lladós, J. (2013). The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recognition, 46(6), 1658-1669. doi:10.1016/j.patcog.2012.11.024Romero, V., Toselli, A. H., & Vidal, E. (2012). Multimodal Interactive Handwritten Text Transcription. Series in Machine Perception and Artificial Intelligence. doi:10.1142/8394Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019Toselli, A. H., Vidal, E., Romero, V., & Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences, 370-371, 497-518. doi:10.1016/j.ins.2016.07.063Bunke, H., Bengio, S., & Vinciarelli, A. (2004). Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 709-720. doi:10.1109/tpami.2004.1