Systematic Textual Availability of Manuscripts (2025)

Abstract: The digital era has made millions of manuscript images in Hebrew available to all. However, despite major advancements in handwritten text recognition over the past decade, an efficient pipeline for large scale and accurate conversion of these manuscripts into useful machine-readable form is still sorely lacking.We propose a pipeline that significantly improves recognition models for automatic transcription of Hebrew manuscripts. Transfer learning is used to fine-tune pretrained models. For post-recognition correction, it leverages text reuse, a common phenomenon in medieval manuscripts, and state-of-the-art large language models for medieval Hebrew.The framework successfully handles noisy transcriptions and consistently suggests alternate, better readings. Initial results show that word level accuracy increased by 10% for new readings proposed by text-reuse detection. Moreover, the character level accuracy improved by 18% by fine-tuning models on the first few pages of each manuscript.

## introduction the survival rate of medieval hebrew manuscripts is much lower than that of latin or arabic texts. thus, the extant hebrew manuscripts-spread out in libraries and private collections worldwide-are a precious asset of historical, cultural and intellectual heritage. the digital era has brought a renaissance to the study of ancient and medieval manuscripts, heretofore available for examination only to limited scholarly circles working at circumscribed locations. recent advancements in digitization have made images of most of the surviving hebrew manuscripts accessible now from every computer, notably through the ktiv project of the national library of israel (national library of israel, 2021). on the order of one hundred thousand manuscripts-comprising more than ten million images-are expected with the completion of the ktiv project. unfortunately, despite major progress in optical character recognition (ocr), an efficient framework for large-scale and accurate conversion of these manuscripts into a machine-readable form remains lacking. the complexity of the materials and the poor quality of many of the items constitute a major hindrance on the way to full textual accessibility. with the rising prominence of artificial neural networks (ann) and their application to handwritten text recognition (htr), the accuracy of the automatic processes is continuously improving @xcite . the tikkun sofrim project @xcite designed and tested an ann based, automatic transcription pipeline for hebrew manuscripts. the project leveraged the open-source tool kraken @xcite , off-the-shelf methods for automatic page segmentation, layout analysis, and line segmentation and developed a tailored crowdsourcing platform to validate and correct automatic transcriptions @xcite . this led to the development of escriptorium @xcite , a virtual research environment, enabling scholars to create a fullfledged transcription. however, kraken is designed to train a specific lstm neural network model for each manuscript. this requires large efforts preparing labeled data for training the model for each manuscript. to dramatically reduce the quantity of manual annotation effort needed to create training sets for handwritten hebrew text recognition, we employ a bank of pretrained models as an ensemble of models in parallel, combining their results. moreover, when minimal labeling of manuscripts is available, we can use transfer learning to refine the accuracy of the pretrained models. the crowdsourcing efforts needed for transcription validation and correction are labor intensive. we aim to increase the pipeline efficiency by dramatically reducing transcription error rate using post-recognition correction algorithms. the most effective method at our disposal for automatically improving transcriptions is the use of sequence alignment methods to line up the imperfectly deciphered texts with the same or with other compositions in existing corpora or previously transcribed manuscripts of similar texts @xcite . this approach was suggested in @xcite and by others. an early work on aligning ocr text with ground-truth (gt) transcriptions is @xcite . highperformance sequence alignment algorithms have long been used. existing text alignment tools, however, generally assume accurate transcriptions, rather than error-riddled post-ocr texts. we propose a text-reuse detection framework designed for medieval hebrew language, which utilizes fuzzy search on the inverted index, followed by an approximate alignment algorithm to handle noisy ocr. we combine this with the use of various state-ofthe-art hebrew language models to propose new and better readings. ## methodology we designed a transcription pipeline that extends the one in @xcite , comprising the following steps: 1. first, manuscript images are needed. we rely on ktiv, which is the midst of the process of digitizing the entire extant hebrew manuscript corpus. 2. the next step is transcription of the text in the manuscript. we use the trained models of kraken to first segment and then transcribe the text appearing in the images. 3. both text-reuse detection and large language models are then applied to propose corrections to several pages of the specific manuscript. 4. based on that, experts correct any remaining transcription errors in those pages. the advanced user interface of @xcite is used for this. 5. the recognition model is fine-tuned based on that ground truth. 6. the refined model is applied to the complete manuscript. 7. experts or crowd-sourcing may be employed to correct any remaining errors. 8. the text-reuse detection framework kicks in again to map all interconnections between the manuscript and other documents in the corpus. 9. finally, the outputs are delivered to humanities researchers. ## experimental setup we perform first-pass htr using both base and fine-tuned htr models, in order to examine how the proposed methods can improve the standard measures, word accuracy rate (war) and character accuracy rate (car). our main metrics are the changes in the accuracy rates, which means that we seek to have the highest possible positive change in war and car. ## conclusions the pipeline proposed here aims to improve the accessibility of historical manuscripts in a machine readable form. text-reuse detection, as a postprocessing component, substantially improves the overall transcription, though it can easily introduce errors. the immediate gains are twofold: (1) the method minimizes the expert manual labor required to validate and correct the transcription, which in turn is utilized to fine-tune the models and improve accuracy. the efficiency of the pipeline we designed depends on the type of the text. (a) manuscripts of familiar works only demand identification of the work and alignment of the entire work with the manuscript text, expected to be produced. work on aligning text with images includes @xcite . (b) manuscripts of an anthological nature demand further scrutiny, identifying the most probable source of each paragraph. (c) compilations will benefit less from the search for textual parallels. it may be expected that with additional fine-tuning of the reference library and with better text-reuse thresholds and language models, the accuracy of the post-processing could be increased further. the work described herein is continuing within the framework of the large-scale midrash erc synergy project @xcite , led by daniel stökl ben ezra, judith olszowy-schlanger, nachum dershowitz, and avi shmidman, in coöperation with moshe lavee and the national library of israel. using the ktiv manuscripts as its starting point, it aims to make the contents of preprint hebrew-character (hebrew, aramaic, judeo-arabic, etc.) manuscripts accessible, with a primary focus on biblical, exegetical, and liturgical manuscripts. model selection, post-ocr correction, and model refinement will be automated. linguistic and paleographic analyses will also be performed.