二值化相机图像的OCR

Proceedings of Sixth International Conference on Document Analysis and Recognition Pub Date : 2001-09-10 DOI:10.1109/ICDAR.2001.953754

M. Seeger, C. Dance

{"title":"二值化相机图像的OCR","authors":"M. Seeger, C. Dance","doi":"10.1109/ICDAR.2001.953754","DOIUrl":null,"url":null,"abstract":"We describe a binarisation method designed specifically for OCR of low quality camera images: background surface thresholding or BST. This method is robust to lighting variations and produces images with very little noise and consistent stroke width. BST computes a \"surface\" of background intensities at every point in the image and performs adaptive thresholding based on this result. The surface is estimated by identifying regions of low-resolution text and interpolating neighbouring background intensities into these regions. The final threshold is a combination of this surface and a global offset. According to our evaluation BST produces considerably fewer OCR errors than Niblack's local average method while also being more runtime efficient.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"75","resultStr":"{\"title\":\"Binarising camera images for OCR\",\"authors\":\"M. Seeger, C. Dance\",\"doi\":\"10.1109/ICDAR.2001.953754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a binarisation method designed specifically for OCR of low quality camera images: background surface thresholding or BST. This method is robust to lighting variations and produces images with very little noise and consistent stroke width. BST computes a \\\"surface\\\" of background intensities at every point in the image and performs adaptive thresholding based on this result. The surface is estimated by identifying regions of low-resolution text and interpolating neighbouring background intensities into these regions. The final threshold is a combination of this surface and a global offset. According to our evaluation BST produces considerably fewer OCR errors than Niblack's local average method while also being more runtime efficient.\",\"PeriodicalId\":277816,\"journal\":{\"name\":\"Proceedings of Sixth International Conference on Document Analysis and Recognition\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"75\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Sixth International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2001.953754\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Sixth International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2001.953754","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 75

摘要

我们描述了一种专为低质量相机图像的OCR设计的二值化方法:背景表面阈值或BST。该方法对光照变化具有鲁棒性，并且产生的图像具有非常小的噪声和一致的笔画宽度。BST在图像中的每个点计算背景强度的“表面”，并基于该结果执行自适应阈值分割。通过识别低分辨率文本的区域并将邻近的背景强度插值到这些区域来估计表面。最后的阈值是这个表面和一个全局偏移量的组合。根据我们的评估，BST比Niblack的局部平均方法产生的OCR错误要少得多，同时运行时效率也更高。

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

Binarising camera images for OCR

We describe a binarisation method designed specifically for OCR of low quality camera images: background surface thresholding or BST. This method is robust to lighting variations and produces images with very little noise and consistent stroke width. BST computes a "surface" of background intensities at every point in the image and performs adaptive thresholding based on this result. The surface is estimated by identifying regions of low-resolution text and interpolating neighbouring background intensities into these regions. The final threshold is a combination of this surface and a global offset. According to our evaluation BST produces considerably fewer OCR errors than Niblack's local average method while also being more runtime efficient.

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

Proceedings of Sixth International Conference on Document Analysis and Recognition

自引率

0.00%

发文量