HTR for Greek Historical Handwritten Documents

Tsochatzidis, Lazaros; Symeonidis, Symeon; Papazoglou, Alexandros; Pratikakis, Ioannis

doi:10.3390/jimaging7120260

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…This technique effectively reduces the total number of parameters while simultaneously enhancing the overall performance of the model. Authors of [15,60] presented Convolutional Recurrent Neural Network (CRNN) architecture; the latter utilized CRNN for handwriting recognition as an encoder for the input text lines while utilizing a Bidirectional Long Short-Term Memory (BLSTM) network followed by a fully CNN as a decoder to predict the sequence of characters. IAM and Reconnaissance et Indexation de données Manuscrites et de fac similÉS (RIMES) [61] with the newly created dataset (EPARCHOS) [60] that includes historical Greek manuscripts have been used in the evaluation process of the proposed architecture.…”

Section: Advancements In Handwritten Textmentioning

confidence: 99%

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

AlKendi,

Gechter,

Heyberger

et al. 2024

J. Imaging

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Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives. In this study, we introduce a hybrid form archive written in French: the Belfort civil registers of births. The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations. The objective of this survey paper is to summarize research on handwritten text documents and provide research directions toward effectively transcribing this French dataset. To achieve this goal, we presented a brief survey of several modern and historical HTR offline systems of different international languages, and the top state-of-the-art contributions reported of the French language specifically. The survey classifies the HTR systems based on techniques employed, datasets used, publication years, and the level of recognition. Furthermore, an analysis of the systems’ accuracies is presented, highlighting the best-performing approach. We have also showcased the performance of some HTR commercial systems. In addition, this paper presents a summarization of the HTR datasets that publicly available, especially those identified as benchmark datasets in the International Conference on Document Analysis and Recognition (ICDAR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR) competitions. This paper, therefore, presents updated state-of-the-art research in HTR and highlights new directions in the research field.

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Section: Advancements In Handwritten Textmentioning

confidence: 99%

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

AlKendi,

Gechter,

Heyberger

et al. 2024

J. Imaging

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“…The most recent bibliography suggests applications such as Tesseract [Patel et al, 2012;White, 2012], 4 Kraken [Schoen and Saretto, 2022;Kiessling, 2019], 5 eScriptorium [Kiessling et al, 2019], Transkribus [Kahle et al, 2017;Muehlberger et al, 2019], or μDoc [Tsochatzidis et al, 2021]. Despite being listed in line here, it should be noted that the above methods cannot be measured reliably against each other, as they are diverse in architecture and function (i.e., Kraken and Tesseract are OCR engines, while eScriptorium and Transkribus are interface platforms for HTR).…”

Section: Literature Reviewmentioning

confidence: 99%

“…6 http://web.archive.org/web/20211113063459/https:/readcoop.eu/transkribus/download/ (Accessed: 19 July 2022). 7 http://web.archive.org/web/20220119164148/https:/transkribus.eu/lite/ (Accessed: 19 July 2022).8 An HTR application for Byzantine manuscripts was described by[Tsochatzidis et al, 2021], but it is currently unavailable to the public.…”

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confidence: 99%

Preparing Big Manuscript Data for Hierarchical Clustering with Minimal HTR Training

Perdiki

2023

Journal of Data Mining &Amp; Digital Humanities

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HTR (Handwritten Text Recognition) technologies have progressed enough to offer high-accuracy results in recognising handwritten documents, even on a synchronous level. Despite the state-of-the-art algorithms and software, historical documents (especially those written in Greek) remain a real-world challenge for researchers. A large number of unedited or under-edited works of Greek Literature (ancient or Byzantine, especially the latter) exist to this day due to the complexity of producing critical editions. To critically edit a literary text, scholars need to pinpoint text variations on several manuscripts, which requires fully (or at least partially) transcribed manuscripts. For a large manuscript tradition (i.e., a large number of manuscripts transmitting the same work), such a process can be a painstaking and time-consuming project. To that end, HTR algorithms that train AI models can significantly assist, even when not resulting in entirely accurate transcriptions. Deep learning models, though, require a quantum of data to be effective. This, in turn, intensifies the same problem: big (transcribed) data require heavy loads of manual transcriptions as training sets. In the absence of such transcriptions, this study experiments with training sets of various sizes to determine the minimum amount of manual transcription needed to produce usable results. HTR models are trained through the Transkribus platform on manuscripts from multiple works of a single Byzantine author, John Chrysostom. By gradually reducing the number of manually transcribed texts and by training mixed models from multiple manuscripts, economic transcriptions of large bodies of manuscripts (in the hundreds) can be achieved. Results of these experiments show that if the right combination of manuscripts is selected, and with the transfer-learning tools provided by Transkribus, the required training sets can be reduced by up to 80%. Certain peculiarities of Greek manuscripts, which lead to easy automated cleaning of resulting transcriptions, could further improve these results. The ultimate goal of these experiments is to produce a transcription with the minimum required accuracy (and therefore the minimum manual input) for text clustering. If we can accurately assess HTR learning and outcomes, we may find that less data could be enough. This case study proposes a solution for researching/editing authors and works that were popular enough to survive in hundreds (if not thousands) of manuscripts and are, therefore, unfeasible to be evaluated by humans.

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“…By getting closer to the textual content of these documents, numerous AI-based approaches for optical character recognition (OCR) and handwritten text recognition (HTR) have been proposed, with deep learning-based approaches (Jaderberg et al, 2016) setting new standards. More advanced deep learning techniques rely on Recurrent Neural Networks (RNN) (Tsochatzidis et al, 2021;Fischer, 2020;Puigcerver, 2017) and Gated-CNNS (Kang et al, 2020;de Sousa Neto et al, 2020;Bluche & Messina, 2017); most recently, transformer-based architectures have set new benchmarks (Wick et al, 2021;Li et al, 2021;Ströbel et al, 2022). Beyond OCR and HTR tasks, AI approaches are emerging as a leading method in text restoration and reconstruction, which is vital when working with often fragmentary historical data.…”

Section: Introductionmentioning

confidence: 99%

Explainability and transparency in the realm of digital humanities: toward a historian XAI

El-Hajj,

Eberle,

Merklein

et al. 2023

Int J Digit Humanities

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The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, as well as its heterogeneity. Despite the scarcity of data it has become common practice to design increasingly complex AI models, usually at the expense of human readability, explainability, and trust. This in turn has led to an increased need for tools to help humanities scholars better explain and validate their models as well as their hypotheses. In this paper, we discuss the importance of employing Explainable AI (XAI) methods within the humanities to gain insights into historical processes as well as ensure model reproducibility and a trustworthy scientific result. To drive our point, we present several representative case studies from the Sphaera project where we analyze a large, well-curated corpus of early modern textbooks using an AI model, and rely on the XAI explanatory outputs to generate historical insights concerning their visual content. More specifically, we show that XAI can be used as a partner when investigating debated subjects in the history of science, such as what strategies were used in the early modern period to showcase mathematical instruments and machines.

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HTR for Greek Historical Handwritten Documents

Cited by 9 publications

References 18 publications

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Preparing Big Manuscript Data for Hierarchical Clustering with Minimal HTR Training

Explainability and transparency in the realm of digital humanities: toward a historian XAI

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