{"id":6162,"date":"2024-02-19T08:53:12","date_gmt":"2024-02-19T07:53:12","guid":{"rendered":"https:\/\/www.imagineright.com\/?p=6162"},"modified":"2025-09-16T13:17:31","modified_gmt":"2025-09-16T11:17:31","slug":"how-to-improve-ocr-efficiency-in-vim-opentext-on-sap","status":"publish","type":"post","link":"https:\/\/www.imagineright.com\/en\/how-to-improve-ocr-efficiency-in-vim-opentext-on-sap\/","title":{"rendered":"How to improve OCR efficiency in VIM Opentext on SAP"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">One of the key aspects for the automatic invoicing process with <a href=\"https:\/\/www.imagineright.com\/en\/sap-vim-by-opentext\/\" data-type=\"link\" data-id=\"https:\/\/www.imagineright.com\/sap-vim-by-opentext\/\">Vendor Invoice Management (VIM)<\/a> is to accurately extract information using OCR from invoice images. Without a high percentage of invoices being correctly extracted, we won&#8217;t achieve a high percentage of invoices being automatically processed.<\/p>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"tools-to-improve-ocr-efficiency\" title=\"Tools to improve OCR efficiency\">Tools to improve OCR efficiency<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To enhance the efficiency of extracting data from supplier invoices, carried out by OCR solutions like Opentext&#8217;s Intelligent Capture or Business Center Capture, there are various tools available that allow us to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enhance information stored in the vendor master.<\/li>\n\n\n\n<li>Modify information in staging tables.<\/li>\n\n\n\n<li>Train invoices.<\/li>\n\n\n\n<li>Define new client rules for information extraction.<\/li>\n\n\n\n<li>Adjust the quality of invoice scanning.<\/li>\n\n\n\n<li>All the above tools are ineffective if we don&#8217;t know where to apply them.<\/li>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-can-we-do-this\" title=\"How can we do this?\">How can we do this?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before initiating any improvement process, it&#8217;s crucial <strong>to accurately identify what&#8217;s failing <\/strong>to decide where to start. <strong>A report providing real data<\/strong> on errors is necessary, rather than relying solely on user experience or perception of the most frequent errors. This report also enables us to measure the effectiveness of implemented improvements.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Since information extraction relies on the format of invoices generated by suppliers, <strong>the report needs to generate KPIs by supplier<\/strong>, such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>% of invoices with errors<\/li>\n\n\n\n<li>% of erroneous fields<\/li>\n\n\n\n<li>% of errors per field<\/li>\n<\/ul>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">With this information, we can quantitatively measure extraction failures and prioritize the most efficient improvements.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The standard VIM solution lacks a report showing aggregated information by supplier. In the <strong>Information Extraction Service<\/strong>, configuring IES Analytics is possible, but it doesn&#8217;t include aggregated supplier information.<\/p>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ocr-efficiency-report\" title=\"OCR efficiency report\">OCR efficiency report<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At ImagineRight, we&#8217;ve developed a report that measures OCR efficiency and generates KPIs from the information stored by the standard. The report is independent of the adopted extraction solution and works for both Business Center Capture (BCC) and Information Extraction Service (IES).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The report consists of t<strong>wo levels of aggregation<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Aggregation by supplier<\/li>\n\n\n\n<li>Detail by invoice<\/li>\n<\/ul>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">In the <strong>supplier aggregation<\/strong>, defined KPIs are displayed, along with the total number of invoices and fields extracted.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><img fetchpriority=\"high\" decoding=\"async\" alt=\"Interfaz de usuario gr\u00e1fica, Aplicaci\u00f3n\n\nDescripci\u00f3n generada autom\u00e1ticamente\" src=\"https:\/\/lh7-us.googleusercontent.com\/RLo0gCcFaZmEPFGcTt_0miEl4vtNY94zJlHoNjYJsRiUWPldH3YlTwgtmTWrS8KJyaqY6XcU5uU8jMYJv3sUcgpxscxgrStD0mA1FRBJypW0qcHXoaFrAEo_XHNvkfMI57kODqZ1O8Z_pf9OHvmDCg\" width=\"567\" height=\"115\" title=\"\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At a <strong>detailed level<\/strong>, we can compare the value extracted by OCR with the correctly validated value by identifying invoices with extraction errors.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><img decoding=\"async\" alt=\"Tabla\n\nDescripci\u00f3n generada autom\u00e1ticamente\" src=\"https:\/\/lh7-us.googleusercontent.com\/k2WxAFMKloLbglzN_Rs5UYiGS_jUjqu_H6yQBjEISMtnJXt6wzQFURPdsFlO9Qf93otEv7Kphcw20C42e-Dn-4OAa7J7j-hAkZnn9QI70RRv7UlPOKFgoCTFMkr0lvo9ldnaNvEC3uTTZPViMrHMew\" width=\"454\" height=\"245\" title=\"\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The report allows navigation to the VIM document to view the document image.<\/p>\n\n\n\n<div style=\"height:35px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"improvement-process\" title=\"Improvement process\">Improvement process<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">There are numerous factors influencing data extraction using OCR, hence, <strong>improvements to implement can&#8217;t be predefined<\/strong>. Prior analysis is necessary to identify areas for improvement. The steps to follow for implementing OCR data extraction improvements are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Define a <strong>starting point <\/strong>by selecting an initial period.<\/li>\n\n\n\n<li>Execute the <strong>OCR Efficiency report<\/strong> developed by ImagineRight.<\/li>\n\n\n\n<li><strong>Identify <\/strong>fields with the highest number of extraction errors.<\/li>\n\n\n\n<li>For these fields, <strong>select the suppliers <\/strong>responsible for 80% of the errors.<\/li>\n\n\n\n<li><strong>Implement <\/strong>improvements best suited to the selected suppliers.<\/li>\n\n\n\n<li><strong>Evaluate<\/strong> the efficiency report after a period with implemented improvements.<\/li>\n\n\n\n<li>If the solution doesn&#8217;t meet expectations, iterate the process again.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">As mentioned earlier, a high degree of autoposting depends not only on a high percentage of correctly extracted invoices. Following improvements in OCR, <strong>we are now able to address an automated invoicing improvement process in VIM<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At ImagineRight, we assist in optimizing the efficiency of <a href=\"https:\/\/www.imagineright.com\/en\/sap-vim-by-opentext\/\" data-type=\"link\" data-id=\"https:\/\/www.imagineright.com\/en\/sap-vim-by-opentext\/\">VIM Opentext OCR on SAP<\/a>. Our personalized OCR efficiency report provides a detailed insight into extraction errors, facilitating identification of improvement areas and prioritization of actions to achieve the best outcomes. Contact us for further information.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>One of the key aspects for the automatic invoicing process with Vendor Invoice Management (VIM) is to accurately extract information using OCR from invoice images. Without a high percentage of invoices being correctly extracted, we won&#8217;t achieve a high percentage of invoices being automatically processed. Tools to improve OCR efficiency To enhance the efficiency of [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":6160,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[108,84,85],"tags":[],"class_list":["post-6162","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-opentext-en","category-sap-en","category-vim-en"],"_links":{"self":[{"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/posts\/6162","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/comments?post=6162"}],"version-history":[{"count":2,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/posts\/6162\/revisions"}],"predecessor-version":[{"id":6164,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/posts\/6162\/revisions\/6164"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/media\/6160"}],"wp:attachment":[{"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/media?parent=6162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/categories?post=6162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.imagineright.com\/en\/wp-json\/wp\/v2\/tags?post=6162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}