Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives.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 The digitization of these historical documents is challenging due to their u...

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Bibliographic Details
Published in:Journal of imaging 2024-01, Vol.10 (1), p.18
Main Authors: AlKendi, Wissam, Gechter, Franck, Heyberger, Laurent, Guyeux, Christophe
Format: Article
Language:English
Subjects:
19th century
Algorithms
Analysis
Annotations
Arabic language
Archives & records
Belfort civil registers of births
Births
Datasets
Digitization
French language
Handwriting
Handwriting recognition
handwritten text recognition (HTR)
HTR datasets
ICDAR
ICFHR
International conferences
Literature reviews
machine learning
Neural networks
Object recognition (Computers)
Pattern recognition
Penmanship
Polls & surveys
Systematic review
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Summary:Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives.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 The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations.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.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging10010018