Comparative Analysis of Handwritten Text Recognition Across Cyrillic-Using Countries

Burul Shambetova (), Ruslan Isaev, Nazira Abdillaeva and Mekia Shigute Gaso
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Burul Shambetova: Ala-Too International University, Computer Science Department
Ruslan Isaev: Ala-Too International University, Computer Science Department
Nazira Abdillaeva: Ala-Too International University, Computer Science Department
Mekia Shigute Gaso: Ala-Too International University, Computer Science Department

A chapter in Technological Innovations for Sustainable Development, 2025, pp 314-327 from Springer

Abstract: Abstract The paper presents an analysis of Cyrillic Handwritten Text Recognition (HTR), high-lighting the unique challenges and technological advancements in this field across various countries. Despite the significance of HTR for digitizing historical documents and automating document processing, progress remains limited compared to Latin script, primarily due to the complexity of the Cyrillic character set and the variability in handwritten styles. The study underscores the achievements in Russian HTR, particularly through the adoption of advanced deep learning models, including CNNs, RNNs, and Transformer-based approaches. However, it identifies ongoing issues such as the scarcity of high-quality, labeled datasets and the need for more robust recognition systems. Comparative insights from other countries, including Kyrgyzstan, Uzbekistan, and additional Cyrillic-using nations such as Bulgaria, Serbia, North Macedonia, Belarus, Tajikistan, Ukraine, and Transnistria, reveal a common struggle with resource limitations and data availability. The paper advocates for increased investments and international collaboration to develop comprehensive datasets and enhance recognition technologies, ultimately aiming to improve the efficiency and accessibility of digital document processing for Cyrillic script users.

Keywords: Cyrillic script; HTR; OCR; Kyrgyz language; NLP; Machine Learning; Deep Learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-06725-8_27

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