BPTI: bilingual printed text images dataset for recognition purposes

M. Yahia, H. Al-Muhtaseb
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引用次数: 0

Abstract

Datasets of text images are important for optical text recognition systems. Such datasets can be used to enhance performance and recognition rates. In this research work, we present a bilingual dataset consists of Arabic/English text images to address the lack of availability of bilingual text databases. The presented dataset consists of 97812 text images, which are categorized into two groups; Scanned page and digitized line images. Images of the two forms are written with 10 fonts and four sizes, and prepared/scanned with four dpi resolutions. The dataset preparation process includes text collection, text editing, image construction, and image processing. The dataset can be used in optical text recognition, optical font recognition, language identification, and segmentation. Different text recognition and language identification experiments have been conducted using images of the dataset and Hidden Markov Model (HMM) classifier. For the digitized images recognition experiments, the best-achieved recognition correctness is 99.01% and the best accuracy is 99.01%. The font that has the highest recognition rates was Tahoma. For the scanned images recognition experiments, Tahoma has also shown the highest performance with 97.86% for correctness and 97.73% for accuracy. For the language identification experiments, Tahoma has shown the performance with 99.98% for word-language identification rate.
用于识别目的的双语印刷文本图像数据集
文本图像数据集是光学文本识别系统的重要组成部分。这样的数据集可以用来提高性能和识别率。在这项研究工作中,我们提出了一个由阿拉伯语/英语文本图像组成的双语数据集,以解决双语文本数据库可用性不足的问题。该数据集包含97812张文本图像,分为两组;扫描页面和数字化线图像。这两种表格的图像用10种字体和4种尺寸书写,并以4种dpi分辨率准备/扫描。数据集准备过程包括文本收集、文本编辑、图像构建和图像处理。该数据集可用于光学文本识别、光学字体识别、语言识别和切分。使用该数据集的图像和隐马尔可夫模型(HMM)分类器进行了不同的文本识别和语言识别实验。在数字化图像识别实验中,最佳识别率为99.01%,最佳识别率为99.01%。识别率最高的字体是Tahoma。在扫描图像识别实验中,Tahoma也表现出了最高的性能,其正确性为97.86%,准确性为97.73%。在语言识别实验中,Tahoma的词-语言识别率达到了99.98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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