Abstract
Solving the problem of Optical Character Recognition (OCR) on printed text for Latin and its derivative scripts can now be considered settled due to the volumes of research done on English and other High-Resourced Languages (HRL). However, for Low-Resourced Languages (LRL) that use unique scripts, it remains an open problem. This study presents a comparative analysis of the zero-shot performance of six distinct OCR engines on two LRLs: Sinhala and Tamil. The selected engines include both commercial and open-source systems, aiming to evaluate the strengths of each category. The Cloud Vision API, Surya, Document AI, and Tesseract were evaluated for both Sinhala and Tamil, while Subasa OCR and EasyOCR were examined for only one language due to their limitations. The performance of these systems was rigorously analysed using five measurement techniques to assess accuracy at both the character and word levels. According to the findings, Surya delivered the best performance for Sinhala across all metrics, with a WER of 2.61%. Conversely, Document AI excelled across all metrics for Tamil, highlighted by a very low CER of 0.78%. In addition to the above analysis, we also introduce a novel synthetic Tamil OCR benchmarking dataset.
Abstract (translated)
解决拉丁语及其衍生脚本印刷文本的光学字符识别（OCR）问题，由于对英语及其他高资源语言（HRL）进行了大量的研究工作，现在可以被认为是基本解决了。然而，对于使用独特脚本的低资源语言（LRL），这一问题仍然存在挑战性。这项研究通过对斯里兰卡语和泰米尔语这两种LRL进行比较分析，探讨了六种不同OCR引擎在零样本设置下的性能表现。所选的系统包括商业和开源两类，旨在评估每类系统的优缺点。对于斯里兰卡语和泰米尔语，分别测试了Cloud Vision API、Surya、Document AI以及Tesseract这四个系统；而由于Subasa OCR和EasyOCR的局限性，仅对其中一个语言进行了评测。通过五种不同的度量方法来评估这些系统的字符级和词级准确率，并对其性能进行了严格分析。 研究结果表明，在所有指标中，Surya在斯里兰卡语上表现最佳，其词错误率为2.61%；而在泰米尔语方面，Document AI则在所有测量标准下均表现出色，尤其值得一提的是其字符错误率仅为0.78%。此外，除了上述分析外，我们还引入了一套全新的合成泰米尔语OCR基准数据集。
URL
https://arxiv.org/abs/2507.18264
