Abstract
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.
Abstract (translated)
巴纳语（Bahnar）是越南、柬埔寨和老挝地区的一种少数民族语言，面临着由于研究不足和数据稀缺而导致的保存挑战。本研究旨在通过光学字符识别（OCR）技术准确地对巴纳语文档进行数字化处理，以解决这一关键问题。将纸质文件扫描并数字化的过程中存在重大挑战，因为破损或模糊区域导致图像质量下降，从而引入了大量的OCR错误，影响信息检索系统的准确性。 我们提出了一种结合先进的表格和非表格检测方法与基于概率的后处理启发式算法的全面解决方案，以提高识别精度。该方法首先使用检测算法来改进输入数据的质量，然后在OCR输出上应用概率误差校正。实验结果表明，在采用上述方案之后，识别准确率从72.86%提升到了79.26%，取得了显著的进步。 这项工作为巴纳语的保存提供了宝贵的资源，并且也为其他少数民族语言的数字化努力提供了一个适用框架。
URL
https://arxiv.org/abs/2601.02965
