{"title":"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","authors":"Prarthana Dutta, Naresh Babu Muppalaneni","doi":"10.1007/s12652-024-04805-y","DOIUrl":null,"url":null,"abstract":"<p>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 <i>Raster Scanning</i> 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.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A top-down character segmentation approach for Assamese and Telugu handwritten documents\",\"authors\":\"Prarthana Dutta, Naresh Babu Muppalaneni\",\"doi\":\"10.1007/s12652-024-04805-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 <i>Raster Scanning</i> 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.</p>\",\"PeriodicalId\":14959,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Humanized Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Humanized Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12652-024-04805-y\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04805-y","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
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.
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):
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