Problem
Research questions and friction points this paper is trying to address.
Optical Character Recognition
Indic Manuscripts
Multi-Script
UniLipi: A Unified Multi-Script OCR for Historical Indic Manuscripts
🤖 AI Summary
为解决历史印度手稿多文字识别难题，提出UniLipi模型，通过统一框架训练13种印度文字，并利用合成数据减少对大量真实标注数据的依赖。
📝 Abstract
Optical character recognition (OCR) for handwritten Indic manuscripts is essential for large-scale digitization and computational access to manuscript heritage. However, existing approaches are typically developed for one script at a time and require substantial script-specific customization. This limits scalability and practical deployment across diverse collections. We present UniLipi, a unified multi-script OCR model for handwritten Indic manuscripts trained jointly across 13 Indic scripts within a single framework. UniLipi directly handles realistic manuscript conditions, including extreme variation in line geometry, large variation in line length, and partial interruptions caused by non-textual manuscript entities such as holes, stains, or pictorial illustrations. To operate effectively under ultra low-resource conditions, the model leverages script-aware synthetic manuscript data generation, substantially reducing reliance on large volumes of real annotated data. Beyond historical manuscripts, we show that UniLipi serves as an effective foundational pretrained model. Specifically, its learned representations enable good OCR performance for contemporary Indic handwriting and extend to several non-Indic scripts, including Tibetan, Italian, Latin, and Chinese scripts. In addition to transcription, UniLipi predicts script identity and per-line native character counts, supporting practical manuscript cataloging workflows.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unified Multi-Script OCR
Synthetic Manuscript Data Generation
Low-Resource Conditions
Manuscript Cataloging
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