用Tesseract和人工神经网络评价尼泊尔文字OCR的性能

2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) Pub Date : 2018-10-01 DOI:10.1109/CCCS.2018.8586808

Sudan Prajapati, S. R. Joshi, Aman Maharjan, Bikash Balami

{"title":"用Tesseract和人工神经网络评价尼泊尔文字OCR的性能","authors":"Sudan Prajapati, S. R. Joshi, Aman Maharjan, Bikash Balami","doi":"10.1109/CCCS.2018.8586808","DOIUrl":null,"url":null,"abstract":"This paper evaluates the performance of Nepali Script OCR using Tesseract and ANN. A dataset of 69 Nepali fonts with the 2,484 character samples of consonants was used in the study. With Tesseract, the overall accuracy of 96% was obtained in the training phase and 69% in the testing phase. Similarly, with ANN, an accuracy of 98% was obtained in training and 81% in testing.","PeriodicalId":6570,"journal":{"name":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Evaluating Performance of Nepali Script OCR using Tesseract and Artificial Neural Network\",\"authors\":\"Sudan Prajapati, S. R. Joshi, Aman Maharjan, Bikash Balami\",\"doi\":\"10.1109/CCCS.2018.8586808\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper evaluates the performance of Nepali Script OCR using Tesseract and ANN. A dataset of 69 Nepali fonts with the 2,484 character samples of consonants was used in the study. With Tesseract, the overall accuracy of 96% was obtained in the training phase and 69% in the testing phase. Similarly, with ANN, an accuracy of 98% was obtained in training and 81% in testing.\",\"PeriodicalId\":6570,\"journal\":{\"name\":\"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCCS.2018.8586808\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCCS.2018.8586808","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文利用Tesseract和ANN对尼泊尔文字OCR的性能进行了评价。研究中使用了69种尼泊尔语字体和2484个辅音字符样本的数据集。使用Tesseract，训练阶段的总体准确率为96%，测试阶段的总体准确率为69%。同样，使用人工神经网络，在训练中获得98%的准确率，在测试中获得81%的准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Evaluating Performance of Nepali Script OCR using Tesseract and Artificial Neural Network

This paper evaluates the performance of Nepali Script OCR using Tesseract and ANN. A dataset of 69 Nepali fonts with the 2,484 character samples of consonants was used in the study. With Tesseract, the overall accuracy of 96% was obtained in the training phase and 69% in the testing phase. Similarly, with ANN, an accuracy of 98% was obtained in training and 81% in testing.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)

自引率

0.00%

发文量