A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian

Maarand, Martin; Beyer, Yngvil Nesheim; Kåsen, Andre; Fosseide, Knut T.; Kermorvant, Christopher

doi:10.1007/978-3-031-06555-2_27

Cited by 11 publications

(2 citation statements)

References 17 publications

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“…In [25], authors introduced an open database of historical handwritten documents that are fully annotated in the Norwegian language. To assess the performance of state-of-theart HTR models on their dataset, they conducted a systematic survey of open-source HTR models, including twelve models with different characteristics, Their study highlighted the best performing technique and suggested a combination of different models to further improve performance.…”

Section: Literature Review 21 State-of-the-art Recent Surveysmentioning

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Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives.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 The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations.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: Literature Review 21 State-of-the-art Recent Surveysmentioning

confidence: 99%

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

AlKendi,

Gechter,

Heyberger

et al. 2024

J. Imaging

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“…PyLaia model is similar to the "expert" model presented in [28]. First, images are resized to a fixed height of 128 pixels.…”

Section: Handwritten Text Recognition Modelsmentioning

confidence: 99%

Large-scale genealogical information extraction from handwritten Quebec parish records

Tarride

Maarand

Boillet

et al. 2023

IJDAR

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This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow is able to identify the acts and extract personal information. The workflow is divided into successive steps: page classification, text line detection, handwritten text recognition, named entity recognition and act detection and classification. For all these steps, different machine learning models are compared. Once the information is extracted, validation rules designed by experts are then applied to standardize the extracted information and ensure its consistency with the type of act (birth, marriage, and death). This validation step is able to reject records that are considered invalid or merged. The full workflow has been used to process over two million pages of Quebec parish registers from the 19-20th centuries. On a sample comprising 65% of registers, 3.2 million acts were recognized. Verification of the birth and death acts from this sample shows that 74% of them are considered complete and valid. These records will be integrated into the BALSAC database and linked together to recreate family and genealogical relations at large scale.

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Consistent Nested Named Entity Recognition in Handwritten Documents via Lattice Rescoring

Villanova-Aparisi,

Martínez-Hinarejos,

Romero

et al. 2023

Lecture Notes in Computer Science

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A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian

Cited by 11 publications

References 17 publications

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Large-scale genealogical information extraction from handwritten Quebec parish records

Consistent Nested Named Entity Recognition in Handwritten Documents via Lattice Rescoring

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