synthocr-gen: A synthetic ocr dataset generator for low-resource languages- breaking the data barrier

• URL: http://arxiv.org/abs/2601.16113v1
• Date: Thu, 22 Jan 2026 17:01:33 GMT
• Title: synthocr-gen: A synthetic ocr dataset generator for low-resource languages- breaking the data barrier
• Authors: Haq Nawaz Malik, Kh Mohmad Shafi, Tanveer Ahmad Reshi,
• Abstract summary: We present SynthOCR-Gen, an open-source synthetic OCR dataset generator specifically designed for low-resource languages.<n>Our tool addresses the fundamental bottleneck in OCR development by transforming digital Unicode text corpora into ready-to-use training datasets.<n>We demonstrate the efficacy of our approach by generating a 600,000-sample word-segmented Kashmiri OCR dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Optical Character Recognition (OCR) for low-resource languages remains a significant challenge due to the scarcity of large-scale annotated training datasets. Languages such as Kashmiri, with approximately 7 million speakers and a complex Perso-Arabic script featuring unique diacritical marks, currently lack support in major OCR systems including Tesseract, TrOCR, and PaddleOCR. Manual dataset creation for such languages is prohibitively expensive, time-consuming, and error-prone, often requiring word by word transcription of printed or handwritten text. We present SynthOCR-Gen, an open-source synthetic OCR dataset generator specifically designed for low-resource languages. Our tool addresses the fundamental bottleneck in OCR development by transforming digital Unicode text corpora into ready-to-use training datasets. The system implements a comprehensive pipeline encompassing text segmentation (character, word, n-gram, sentence, and line levels), Unicode normalization with script purity enforcement, multi-font rendering with configurable distribution, and 25+ data augmentation techniques simulating real-world document degradations including rotation, blur, noise, and scanner artifacts. We demonstrate the efficacy of our approach by generating a 600,000-sample word-segmented Kashmiri OCR dataset, which we release publicly on HuggingFace. This work provides a practical pathway for bringing low-resource languages into the era of vision-language AI models, and the tool is openly available for researchers and practitioners working with underserved writing systems worldwide.

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