通过概率后处理提升濒危语言文字识别准确率
Low-Resource Heuristics for Bahnaric Optical Character Recognition Improvement
- 结合表格检测与概率修正提升输入质量
- 识别准确率从72.86%提升至79.26%
- 为少数民族语言数字化提供可复用框架
巴纳尔语是越南、柬埔寨和老挝部分地区使用的少数民族语言，由于研究和数据资源有限，面临严峻的保护挑战。本文针对巴纳尔语文本的光学字符识别（OCR）需求，提出一种综合方法：先通过先进的表格与非表格检测技术改善输入图像质量，再采用基于概率的后处理启发式策略对OCR结果进行纠错。实验表明，该方法显著提升了识别准确率，从72.86%提升至79.26%。本工作不仅为巴纳尔语保护提供了宝贵资源，也为其他少数民族语言的数字化提供了可复用的技术框架。
原文摘要 · Abstract (English)
Bahnar, a minority language spoken across Vietnam, Cambodia, and Laos, faces significant preservation challenges due to limited research and data availability. This study addresses the critical need for accurate digitization of Bahnar language documents through optical character recognition (OCR) technology. Digitizing scanned paper documents poses significant challenges, as degraded image quality from broken or blurred areas introduces considerable OCR errors that compromise information retrieval systems. We propose a comprehensive approach combining advanced table and non-table detection techniques with probability-based post-processing heuristics to enhance recognition accuracy. Our method first applies detection algorithms to improve input data quality, then employs probabilistic error correction on OCR output. Experimental results indicate a substantial improvement, with recognition accuracy increasing from 72.86% to 79.26%. This work contributes valuable resources for Bahnar language preservation and provides a framework applicable to other minority language digitization efforts.
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