A Survey on Methods for Basic Unit Segmentation in Off-line Handwritten Text Recognition

Abliz, Aysadet; Simayi, Wujiahemaiti; Moydin, Kamil; Hamdulla, Askar

doi:10.14257/ijfgcn.2016.9.11.13

Cited by 3 publications

(1 citation statement)

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“…Several methods have been developed to conduct the segmentation process, including threshold methods, region-based methods, edge-based methods, watershed-based methods, and clustering-based methods. The segmentation stage is considered as one of the most crucial steps that can significantly improve the accuracy of HTR models [34].…”

Section: Segmentationmentioning

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Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives.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 The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations.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

confidence: 99%