{"title":"从扫描的PDF文档和MathML转换的数学信息检索(MIR)","authors":"A. Nazemi, I. Murray, D. McMeekin","doi":"10.2197/ipsjtcva.6.132","DOIUrl":null,"url":null,"abstract":"This paper describes part of an ongoing comprehensive research project that is aimed at generating a \nMathML format from images of mathematical expressions that have been extracted from scanned PDF documents. \nA MathML representation of a scanned PDF document reduces the document’s storage size and encodes the math- \nematical notation and meaning. The MathML representation then becomes suitable for vocalization and accessible \nthrough the use of assistive technologies. In order to achieve an accurate layout analysis of a scanned PDF document, \nall textual and non-textual components must be recognised, identified and tagged. These components may be text or \nmathematical expressions and graphics in the form of images, figures, tables and/or diagrams. Mathematical expres- \nsions are one of the most significant components within scanned scientific and engineering PDF documents and need \nto be machine readable for use with assistive technologies. This research is a work in progress and includes multiple \ndifferent modules: detecting and extracting mathematical expressions, recursive primitive component extraction, non- \nalphanumerical symbols recognition, structural semantic analysis and merging primitive components to generate the \nMathML of the scanned PDF document. An optional module converts MathML to audio format using a Text to Speech \nengine (TTS) to make the document accessible for vision-impaired users. \nKeywords: math recognition, graphics recognition, Mathematical Informati","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"23 1","pages":"132-142"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Mathematical Information Retrieval (MIR) from Scanned PDF Documents and MathML Conversion\",\"authors\":\"A. Nazemi, I. Murray, D. McMeekin\",\"doi\":\"10.2197/ipsjtcva.6.132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes part of an ongoing comprehensive research project that is aimed at generating a \\nMathML format from images of mathematical expressions that have been extracted from scanned PDF documents. \\nA MathML representation of a scanned PDF document reduces the document’s storage size and encodes the math- \\nematical notation and meaning. The MathML representation then becomes suitable for vocalization and accessible \\nthrough the use of assistive technologies. In order to achieve an accurate layout analysis of a scanned PDF document, \\nall textual and non-textual components must be recognised, identified and tagged. These components may be text or \\nmathematical expressions and graphics in the form of images, figures, tables and/or diagrams. Mathematical expres- \\nsions are one of the most significant components within scanned scientific and engineering PDF documents and need \\nto be machine readable for use with assistive technologies. This research is a work in progress and includes multiple \\ndifferent modules: detecting and extracting mathematical expressions, recursive primitive component extraction, non- \\nalphanumerical symbols recognition, structural semantic analysis and merging primitive components to generate the \\nMathML of the scanned PDF document. An optional module converts MathML to audio format using a Text to Speech \\nengine (TTS) to make the document accessible for vision-impaired users. \\nKeywords: math recognition, graphics recognition, Mathematical Informati\",\"PeriodicalId\":38957,\"journal\":{\"name\":\"IPSJ Transactions on Computer Vision and Applications\",\"volume\":\"23 1\",\"pages\":\"132-142\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IPSJ Transactions on Computer Vision and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2197/ipsjtcva.6.132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSJ Transactions on Computer Vision and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2197/ipsjtcva.6.132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
Mathematical Information Retrieval (MIR) from Scanned PDF Documents and MathML Conversion
This paper describes part of an ongoing comprehensive research project that is aimed at generating a
MathML format from images of mathematical expressions that have been extracted from scanned PDF documents.
A MathML representation of a scanned PDF document reduces the document’s storage size and encodes the math-
ematical notation and meaning. The MathML representation then becomes suitable for vocalization and accessible
through the use of assistive technologies. In order to achieve an accurate layout analysis of a scanned PDF document,
all textual and non-textual components must be recognised, identified and tagged. These components may be text or
mathematical expressions and graphics in the form of images, figures, tables and/or diagrams. Mathematical expres-
sions are one of the most significant components within scanned scientific and engineering PDF documents and need
to be machine readable for use with assistive technologies. This research is a work in progress and includes multiple
different modules: detecting and extracting mathematical expressions, recursive primitive component extraction, non-
alphanumerical symbols recognition, structural semantic analysis and merging primitive components to generate the
MathML of the scanned PDF document. An optional module converts MathML to audio format using a Text to Speech
engine (TTS) to make the document accessible for vision-impaired users.
Keywords: math recognition, graphics recognition, Mathematical Informati