OCR4all - Open-Source OCR and HTR Across the Centuries

2016

Proceedings IS&T/SPIE 20th Annual Symposium, 2008

Mass-digitization projects in libraries have received world-wide attention. Today a significant amount of historical books, newspapers and journals are already digitized and available online for search and retrieval. The situation is totally different if we look at archives. Though major state and regional archives contain tens or even hundreds of shelf kilometers with most often unique material, the state of digitization lags far behind the library world. One reason may be the absence of a technology that can deal with handwritten documents and convert them into editable text. This paper introduces the concept of a comprehensive Transcription and Recognition Platform (TRP) which may support the development of Handwritten Text Recognition (HTR) technology, based on the assumption that this could best be done within a cooperative model where archives, humanities scholars, computer scientists and the general public are involved and provide specific contributions according to their individual mission and interest.

DigItalia, 2020

Books printed before 1800 present major problems for OCR. One of the main obstacles is the lack of diversity of historical fonts in training data. The OCR-D project, consisting of book historians and computer scientists, aims to address this deficiency by focussing on three major issues. Our first target was to create a tool that identifies font groups automatically in images of historical documents. We concentrated on Gothic font groups that were commonly used in German texts printed in the 15 th and 16 th century: the well-known Fraktur and the lesser known Bastarda, Rotunda, Textura und Schwabacher. The tool was trained with 35,000 images and reaches an accuracy level of 98%. It can not only differentiate between the above-mentioned font groups but also Hebrew, Greek, Antiqua and Italic. It can also identify woodcut images and irrelevant data (book covers, empty pages, etc.). In a second step, we created an online training infrastructure (okralact), which allows for the use of various open source OCR engines such as Tesseract, OCRopus, Kraken and Calamari. At the same time, it facilitates training for specific models of font groups. The high accuracy of the recognition tool paves the way for the unprecedented opportunity to differentiate between the fonts used by individual printers. With more training data and further adjustments, the tool could help to fill a major gap in historical research. OCR-D

2021

Book digitization is being increasingly enhanced, as it facilitates not only the dissemination and preservation of cultural heritage but also the analysis of large amounts of textual data as well as the extraction and discovery of knowledge in a faster, dynamic and interactive way. Quite often, OCR, as the core technology of book digitization, has to address major difficulties related to the condition of the primary source or to scanning issues. The main contribution of this paper is to provide an extensive study on Tesseract, an open-source OCR system, including image pre-processing and text post-processing methods, that overcome a variety of image handling problems. Additionally, a re-trained Greek language model, based on individual fonts training plus pairs of image-text training, is being provided. Finally, this paper proposes a pipeline of methods, including text line detection, that result in enhanced accuracy for Greek Literature documents, even when they consist of distorte...

An essay on Digital Humanities and specifically on the 'new' optimal character recognition (OCR) tool Kraken. Discusses the future research implications that OCR-tools like Kraken might have in the fields of Humanities and Ottoman Studies. Written for the Honoursprogram course Digital Humanities of the University of Amsterdam, graded with a 9 out of 10.

An Open-Source Workflow for Handwritten Character Recognition, 2025

The rich manuscript heritage of Italy, preserved in archives and libraries, is becoming increasingly accessible to a wider audience through dedicated digitization initiatives. However, the interpretation of these manuscripts often proves challenging due to several factors: the linguistic complexity of medieval Latin, the early development of vernacular languages, the continuous evolution of handwriting styles, and the extensive use of abbreviation systems devised to conserve space on costly materials such as parchment and paper. Artificial Intelligence (AI) tools can significantly boost the last step of the digitization process: transcription. In particular, the advent of Handwritten Character Recognition (HCR) technology enables recognition and processing of handwritten text. However, as with all AI tools-especially in the domain of handwritten texts and, more broadly, in the Humanities, training and fine-tuning is required. To support Digital Humanists in tailoring these powerful tools to specific needs-i.e. transcribing different handwriting styles-a Human-AI collaboration approach has been adopted to develop a collaborative web application, named HCR WORKFLOW, designed for the creation of ground-truth data for AI-based manuscript transcription. The platform is composed of a toolkit for document layout analysis based on Neural Networks for text line recognition (P2PaLA), an image Transformer encoder and an autoregressive text Transformer decoder for single-line transcription (TrOCR). This integrated system guides and assists Digital Humanists throughout the entire process-from digitization to transcription supervision. For this study, the platform was used to fine-tune TrOCR on humanistic script, and in particular to create the ground truth based on the Copialettere (Letterbooks) of Isabella d'Este and the letters addressed to her by Lucrezia Borgia. This research paper will discuss in detail the HCR WORKFLOW platform, the dataset used, the approach to create an AI-oriented transcription, and the results of the fine-tuning of the AI tool for manuscript transcription.

2009

This paper describes a work-flow designed to populate a digital library of ancient Greek critical editions with highly accurate OCR scanned text. While the most recently available OCR engines are now able after suitable training to deal with the polytonic Greek fonts used in 19th and 20th century editions, further improvements can also be achieved with postprocessing. In particular, the progressive multiple alignment method applied to different OCR outputs based on the same images is discussed in this paper.

Chicago Colloquium on Digital Humanities and …, 2006

Large-scale digitization of manuscripts is facilitated by high-accuracy optical character recognition (OCR) engines. The focus of our work is on using these tools to digitize Latin texts. Many of the texts in the language, especially the early modern, make heavy use of special characters ...

Journal of data mining and digital humanities, 2024

With this special issue of the Journal of Data Mining and Digital Humanities (JDMDH), we bring together in one single volume several experiments, projects and reflections related to automatic text recognition applied to historical documents. More and more research projects 1 now include automatic text acquisition in their data processing chain, and this is true not only for projects focussed on Digital or Computational Humanities but increasingly also for those that are simply using existing digital tools as the means to an end. The increasing use of this technology has led to an automation of tasks that affects the role of the researcher in the textual production process. This new data-intensive practice makes it urgent to collect and harmonise the corpora necessary for the constitution of training sets, but also to make them available for exploitation. This special issue is therefore an opportunity to present articles combining philological and technical questions to make a scientific assessment of the use of automatic text recognition for ancient documents, its results, its contributions and the new practices induced by its use in the process of editing and exploring texts. We hope that practical aspects will be questioned on this occasion, while raising methodological challenges and its impact on research data. The special issue on Automatic Text Recognition (ATR) is therefore dedicated to providing a comprehensive overview of the use of ATR in the humanities field, particularly concerning historical documents in the early 2020s. This issue presents a fusion of engineering and philological aspects, catering to both beginners and experienced users interested in launching projects with ATR. The collection encompasses a diverse array of approaches, covering topics such as data creation or collection for training generic models, reaching specific objectives, technical and HTR machine architecture, segmentation methods, and image processing.