{"title":"A Case-Based Reasoning Approach for Invoice Structure Extraction","authors":"Hatem Hamza, Y. Belaïd, A. Belaïd","doi":"10.1109/ICDAR.2007.3","DOIUrl":null,"url":null,"abstract":"This paper shows the use of case-based reasoning (CBR) for invoice structure extraction and analysis. This method, called CBR-DIA (CBR for document invoice analysis), is adaptive and does not need any previous training. It analyses a document by retrieving and analysing similar documents or elements of documents (cases) stored in a database. The retrieval step is performed thanks to graph comparison techniques like graph probing and edit distance. The analysis step is done thanks to the information found in the nearest retrieved cases. Applied on 950 invoices, CBR-DIA reaches a recognition rate of 85.29% for documents of known classes and 76.33% for documents of unknown classes.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2007.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
This paper shows the use of case-based reasoning (CBR) for invoice structure extraction and analysis. This method, called CBR-DIA (CBR for document invoice analysis), is adaptive and does not need any previous training. It analyses a document by retrieving and analysing similar documents or elements of documents (cases) stored in a database. The retrieval step is performed thanks to graph comparison techniques like graph probing and edit distance. The analysis step is done thanks to the information found in the nearest retrieved cases. Applied on 950 invoices, CBR-DIA reaches a recognition rate of 85.29% for documents of known classes and 76.33% for documents of unknown classes.
本文介绍了基于案例推理(CBR)在发票结构提取和分析中的应用。这种方法称为CBR- dia (CBR用于文档发票分析)，是自适应的，不需要任何先前的培训。它通过检索和分析存储在数据库中的类似文档或文档元素(案例)来分析文档。检索步骤是通过图探测和编辑距离等图比较技术执行的。由于在最近的检索案例中找到了信息，分析步骤就完成了。应用于950张发票，CBR-DIA对已知类别单据的识别率为85.29%，对未知类别单据的识别率为76.33%。
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