Improved Tesseract optical character recognition performance on Thai document datasets

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Research Pub Date : 2025-02-28 Epub Date: 2025-02-08 DOI:10.1016/j.bdr.2025.100508

Noppol Anakpluek, Watcharakorn Pasanta, Latthawan Chantharasukha, Pattanawong Chokratansombat, Pajaya Kanjanakaew, Thitirat Siriborvornratanakul

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Abstract

This research aims to improve the accuracy and efficiency of Optical Character Recognition (OCR) technology for the Thai language, specifically in the context of Thai government documents. OCR enables the conversion of text from images into machine-readable format, facilitating document storage and further processing. However, applying OCR to the Thai language presents unique challenges due to its complexity. This study focuses on enhancing the performance of the Tesseract OCR engine, a widely used free OCR technology, by implementing various image preprocessing techniques such as masking, adaptive thresholds, median filtering, Canny edge detection, and morphological operators. A dataset of Thai documents is utilized, and the OCR system's output is evaluated using word error rate (WER) and character error rate (CER) metrics. To improve text extraction accuracy, the research employs the original U-Net architecture [19] for image segmentation. Furthermore, the Tesseract OCR engine is finetuned, and image preprocessing is performed to optimize OCR system accuracy. The developed tools automate workflow processes, alleviate constraints on model training, and enable the effective utilization of information from official Thai documents for various purposes.

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改进泰语文档数据集上的Tesseract光学字符识别性能

本研究旨在提高泰国语光学字符识别（OCR）技术的准确性和效率，特别是在泰国政府文件的背景下。OCR可以将图像中的文本转换为机器可读的格式，方便文档存储和进一步处理。然而，由于其复杂性，将OCR应用于泰语面临着独特的挑战。本研究的重点是通过实现各种图像预处理技术，如掩模、自适应阈值、中值滤波、Canny边缘检测和形态学算子，来增强广泛使用的免费OCR技术Tesseract OCR引擎的性能。使用泰语文档的数据集，并使用单词错误率（WER）和字符错误率（CER）指标评估OCR系统的输出。为了提高文本提取的准确性，本研究采用了原始的U-Net架构[19]进行图像分割。此外，对Tesseract OCR引擎进行了微调，并对图像进行了预处理，以优化OCR系统的精度。开发的工具使工作流程自动化，减轻了模型训练的限制，并能够有效地利用泰国官方文档中的信息用于各种目的。

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来源期刊

Big Data Research Computer Science-Computer Science Applications

CiteScore

8.40

自引率

3.00%

发文量

期刊介绍： The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.