针对阿萨姆语和泰卢固语手写文档的自顶向下字符分割方法

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-05-07 DOI:10.1007/s12652-024-04805-y
Prarthana Dutta, Naresh Babu Muppalaneni
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引用次数: 0

摘要

数字化为管理和检索纸质文件所面临的挑战提供了解决方案。但是,这些纸质文件必须转换成数字机器能够理解的格式,因为数字机器主要理解字母数字文本。这种转换是通过光学字符识别(OCR)技术实现的,该技术可将扫描的图像文件转换成机器可以处理的格式。这项工作提出了一种新颖的自上而下的字符分割方法,涉及多个阶段。我们的方法首先从手写文档中分离出线条,然后利用这些线条来分割单词和字符。为了进一步提高字符分割效果,我们采用了光栅扫描对象检测技术来分离单词中的单个字符。因此,字符分割结果是由垂直投影和光栅扫描的结果整合而成的。我们认识到推进手写文件数字化的重要意义,因此选择重点研究阿萨姆邦和安得拉邦的地区语言,因为它们在印度语言多样性中具有重要的历史和文化意义。因此,我们收集了阿萨姆语和泰卢固语的手写文本数据集,因为它们无法以所需的形式提供。我们的方法对这两种语言的行、词和字符的平均分割准确率分别达到了 93.61%、85.96% 和 88.74%。选择自上而下方法的主要动机有两个方面:首先,它提高了字符识别的准确性;其次,通过利用分割的行和字,它有可能在未来用于语言/文字识别。
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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A top-down character segmentation approach for Assamese and Telugu handwritten documents

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India’s linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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