[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-307522-105":53,"doc-detail-307522-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","evaluating-form-designs-for-optical-character-recognition-issue-1-february-1994","Evaluating Form Designs for Optical Character Recognition - Issue 1 - February 1994","","Evaluating Form Designs for Optical Character Recognition analyzes how different form design choices affect the performance of an optical character recognition workflow. The study covers the 1040T forms and database, scoring methods for form-based assessment, and multiple recognition system configurations. Results include configuration observations and field-based study focused on human factors and field performances, followed by analysis of segmentation errors. The report concludes with findings, references, and extensive appendices covering form examples, scoring packages, and system configuration results.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/evaluating-form-designs-for-optical-character-recognition-issue-1-february-1994/307522/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/evaluating-form-designs-for-optical-character-recognition-issue-1-february-1994/307522.png","ImageObject",442,249,{"name":88,"@type":89},"Logic","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-21","2026-09-19",true,{"@type":98,"interactionType":99,"userInteractionCount":9},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What subject does the report address?","Question",{"text":108,"@type":109},"The report evaluates how optical character recognition performance is influenced by form design choices, especially using 1040T forms.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How are recognition system performance and results organized?",{"text":113,"@type":109},"Performance is described through recognition system configurations, configuration observations, field-based study, and field-based performances, followed by segmentation error analysis.",{"name":115,"@type":106,"acceptedAnswer":116},"What supporting materials are included at the end of the report?",{"text":117,"@type":109},"Extensive appendices provide examples of 1040T forms, Billy and Tina reference sets, the NIST scoring package, model recognition system components, and detailed system configuration results, including human factors and segmentation error breakdowns.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},307522,1789961829,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":9,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":141},1099513958762,"https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253","NIST  \nPUBLICATIONS  \n# Evaluating Form Designs forOptical Character Recognition\n\nQC  \n100  \n.U56  \n\\#5364  \n1994  \nMichael D.GarrisDarrin L.Dimmick  \nU.S.DEPARTMENT OF COMMERCETechnology AdministrationNational Institute of Standardsand Technology  \nComputer Systems LaboratoryAdvanced Systems DivisionGaithersburg,MD 20899  \nEvaluating Form Designs forOptical Character Recognition  \nMichael D.Garris  \nU.S.DEPARTMENT OF COMMERCETechnology AdministrationNational Institute of Standardsand Technology  \nComputer Systems LaboratoryAdvanced Systems DivisionGaithersburg,MD 20899  \nFebruary 1994  \nU.S.DEPARTMENT OF COMMERCERonald H.Brown,Secretary  \nTECHNOLOGY ADMINISTRATIONMary L Good,Under Secretary for Technology  \nEvaluating Form Designs For Optical Character RecognitionMichael D.Garris and Darrin L.DimmickNational Institute of Standards and Technology,Gaithersburg,Maryland 20899  \n## TABLE OF CONTENTS\n\n1  \n## ABSTRACT\n\n### 1.INTRODUCTION\n\n1  \n### 2.1040T FORMS AND PERFORMANCE ASSESSMENT\n\n4  \n2.11040T Forms  \n4  \n2.21040T Database  \n4  \n6  \n2.3 Scoring 1040T Forms  \n### 3.RECOGNITION SYSTEM CONFIGURATIONS\n\n7  \n9  \n### 4.RECOGNITION SYSTEM CONFIGURATION RESULTS\n\n4.1 System Configuration Observations  \n11  \n4.2 Field-Based Study  \n12  \n4.2.1 Human Factors  \n13  \n14  \n4.2.2 Field-Based Performances  \n### 5.ANALYSIS OF SEGMENTATION ERRORS\n\n15  \n17  \n6.CONCLUSIONS  \n7.REFERENCES  \n19  \nAPPENDIXA.1040T FORMS  \nA1  \nP11040T Form  \nA3  \nP21040T Form  \nA5  \nP31040T Form  \nA7  \nAPPENDIX B.BILLY AND TINA REFERENCE SETSB1Field-Labeled 1040T FormB3Billy Field Value SetB5Tina Field Value SetB7  \nAPPENDIX C.NIST SCORING PACKAGE  \nC1  \n℃3  \nC.1.Form-Based Scoring  \nC.2.Effects of Rejection  \nAPPENDIX D. MODEL RECOGNITION SYSTEM COMPONENTSD1  \nD.1.Form RegistrationD1  \nD.2.Form RemovalD2  \nD.3.Field IsolationD8  \nD.4.Character Field SegmentationD8  \nD.4.1 Connected Component LabelingD8  \nD.4.2 Form-Based Inter-Character CutsD9  \nD.5.Character Image Spatial NormalizationD11  \nD.5.1 First and Second Generation NormalizationsD11  \nD.5.2 Third Generation NormalizationD12  \nD.6.Character Image Feature ExtractionD14  \nD.7.Character ClassificationD14  \nD.7.1 Multi-Layer PerceptronD14  \nD.7.2 Probabilistic Neural NetworkD15  \nD.8.Icon Field DetectionD16  \nD.8.1 Circle FieldsD16  \nD.8.2 Signature FieldsD17  \nE1  \n## APPENDIX E. SYSTEM CONFIGURATION RESULTS\n\nLegend for Graphs  \nE1  \nSystem Configuration A- Alpha FieldsSystem Configuration A-Float FieldsSystem Configuration A-Integer FieldsSystem Configuration B-Alpha FieldsSystem Configuration B-Float FieldsSystem Configuration B-Integer FieldsSystem Configuration C- Alpha FieldsSystem Configuration C-Float FieldsSystem Configuration C-Integer FieldsSystem Configuration D- Alpha FieldsSystem Configuration D-Float FieldsSystem Configuration D-Integer Fields  \nE2  \nE3  \nE4  \nE5  \nE6  \nE7  \nE8  \nE9  \nE10  \nE11  \nE12  \nE13  \nSystem Configuration E-Alpha FieldsSystem Configuration E-Float FieldsSystem Configuration E-Integer FieldsSystem Configuration F-Alpha FieldsSystem Configuration F-Float FieldsSystem Configuration F-Integer FieldsSystem Mark Detection  \nE14  \nE15  \nE16  \nE17  \nE18  \nE19  \nE20  \nAPPENDIX F.FIELD-BASED RESULTS  \nF1  \nSystem Configuration A-Field p060  \nF2  \nSystem Configuration B-Field p060  \nF3  \nSystem Configuration C-Field p060  \nF4  \nSystem Configuration D-Field p060  \nF5  \nF6  \nSystem Configuration E-Field p060  \nSystem Configuration F-Field p060  \nF7  \nSystem Configuration A-Field p045  \nF8  \nF9  \nSystem Configuration B-Field p045  \nSystem Configuration C-Field p045  \nF10  \nSystem Configuration D-Field p045  \nF11  \nF12  \nSystem Configuration E-Field p045  \nF13  \nSystem Configuration F-Field p045  \nSystem Configuration A-Field p161  \nF14  \nSystem Configuration B-Field p161  \nF15  \nSystem Configuration C-Field p161  \nF16  \nSystem Configuration D-Field p161  \nF17  \nF18  \nSystem Configuration E-Field p161  \nSystem Configuration F-Field p161  \nF19  \nSystem Mark Detection -Fields p023 and p034","cbCaiju01eAwehFR","https://ap.wps.com/l/cbCaiju01eAwehFR","pdf",9914719,130,"English","# Table of Contents\n## Abstract\n## Introduction\n## 1040T Forms and Performance Assessment\n## Recognition System Configurations\n## Recognition System Configuration Results\n## Analysis of Segmentation Errors\n## Conclusions\n## References\n## Appendix A. 1040T Forms\n## Appendix B. Billy and Tina Reference Sets\n## Appendix C. NIST Scoring Package\n## Appendix D. Model Recognition System Components\n## Appendix E. System Configuration Results\n## Appendix F. Field-Based Results\n## Appendix G. Human Factors\n## Appendix H. Segmentation Errors","[{\"question\":\"What subject does the report address?\",\"answer\":\"The report evaluates how optical character recognition performance is influenced by form design choices, especially using 1040T forms.\"},{\"question\":\"How are recognition system performance and results organized?\",\"answer\":\"Performance is described through recognition system configurations, configuration observations, field-based study, and field-based performances, followed by segmentation error analysis.\"},{\"question\":\"What supporting materials are included at the end of the report?\",\"answer\":\"Extensive appendices provide examples of 1040T forms, Billy and Tina reference sets, the NIST scoring package, model recognition system components, and detailed system configuration results, including human factors and segmentation error breakdowns.\"}]","Evaluating Form Designs for Optical Character Recognition - Issue 1 - February 1994 | PDF",1789850896,46]