{"id":425,"date":"2024-01-04T17:27:18","date_gmt":"2024-01-04T17:27:18","guid":{"rendered":"https:\/\/physicaldb.ece.ufl.edu\/?page_id=425"},"modified":"2026-09-26T01:12:59","modified_gmt":"2026-09-26T01:12:59","slug":"elementor-425","status":"publish","type":"page","link":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/elementor-425\/","title":{"rendered":"TEXT RECOGNITION FOR PCBS_Dataset"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"425\" class=\"elementor elementor-425\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0e83575 e-flex e-con-boxed e-con e-parent\" data-id=\"0e83575\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6f4c9b0 elementor-widget elementor-widget-html\" data-id=\"6f4c9b0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!DOCTYPE html>\r\n<html>\r\n\r\n<head>\r\n    <title>TEXT RECOGNITION FOR PCBS_Dataset_Dataset<\/title>\r\n    <style>\r\n        table {\r\n            background-color: #f0f0f0;\r\n            border-collapse: collapse;\r\n            width: 100%;\r\n        }\r\n\r\n        th,\r\n        td {\r\n            padding: 50px;\r\n            border: 1px solid #ddd;\r\n            text-align: left;\r\n            font-size: x-large;\r\n        }\r\n\r\n        th {\r\n            background-color: #ccc;\r\n        }\r\n\r\n        body {\r\n            font-family: Arial, sans-serif;\r\n        }\r\n\r\n        .popup-background {\r\n            display: none;\r\n            position: fixed;\r\n            top: 0;\r\n            left: 0;\r\n            width: 100%;\r\n            height: 100%;\r\n            background-color: rgba(0, 0, 0, 0.7);\r\n            z-index: 1;\r\n        }\r\n\r\n        .popup-content,\r\n        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        color: white;\r\n            \/* White text *\/\r\n            padding: 10px 15px;\r\n            \/* Padding around the text *\/\r\n            font-size: 20px;\r\n            \/* Font size *\/\r\n            border: none;\r\n            \/* No border *\/\r\n            border-radius: 5px;\r\n            \/* Rounded corners *\/\r\n            cursor: pointer;\r\n            \/* Pointer on hover *\/\r\n            outline: none;\r\n            \/* No outline when focused *\/\r\n            margin-top: 10px;\r\n            \/* Space from the top *\/\r\n        }\r\n\r\n        #citeBtn:hover {\r\n            background-color: #218838;\r\n            \/* Darker green on hover *\/\r\n        }\r\n\r\n        .icon-text {\r\n            vertical-align: middle;\r\n            margin-left: 5px;\r\n            cursor: pointer;\r\n            font-size: 16px;\r\n            \/* Adjust icon size *\/\r\n        }\r\n    <\/style>\r\n<\/head>\r\n\r\n<body>\r\n    <h1>TEXT RECOGNITION FOR PCBS<\/h1>\r\n\r\n    <table>\r\n        <tr>\r\n            <th>Description<\/th>\r\n            <td>The document titled \"Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection\": A Survey focuses on the challenges and methodologies of automatic text recognition on printed circuit boards (PCBs). It highlights the significance of accurately recognizing component markings and reference designators for effective automatic Bill of Materials extraction and hardware assurance. The survey explores various state-of-the-art end-to-end text recognition approaches, emphasizing the unique challenges posed by PCBs. It also discusses the criticality of data collection with appropriate annotations and presents edge cases with insights for future research. The importance of developing specialized algorithms for PCB marking recognition, integrating self-explainability, and the motivation for further research in this domain are key elements of the study. <\/td>\r\n\r\n         \r\n        <\/tr>\r\n        <tr>\r\n            <th>Downloads<\/th>\r\n            <td><a href=\"#\" id=\"downloadLink\">TEXT RECOGNITION FOR PCBS<\/a><\/td>\r\n        <\/tr>\r\n        <tr>\r\n            <th>Citations<\/th>\r\n            <td>\r\n                <!-- Citation Button -->\r\n                <button id=\"citeBtn\"><b>Cite This<\/b><\/button>\r\n                <a href=\"Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey\" target=\"_blank\">\r\n                    <button class=\"paperBtn\" >Paper<\/button>\r\n                <\/a>\r\n                <!-- Citation Popup -->\r\n                <div id=\"citeModal\" class=\"cite-popup-background\">\r\n                    <div class=\"cite-popup-box\">\r\n                        <span class=\"cite-close-button\">&times;<\/span>\r\n                        <h2>Citation Formats<\/h2>\r\n                        <table class=\"citation-table\">\r\n                            <!-- Table Headings -->\r\n                            <tr>\r\n                                <th>Text Format<\/th>\r\n                                <th>BibTeX<\/th>\r\n                            <\/tr>\r\n                            <!-- Table Body with citation content -->\r\n                            <tr>\r\n                                <td id=\"textCitation\">\r\n                                S. Ghosh, M. A. M. Sathiaseelan and N. Asadizanjani,\r\n                                 <a href=\"Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey\" > \r\n                                    \"Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey,\"<\/a> \r\n                                    2021 IEEE Physical Assurance and Inspection of Electronics (PAINE), Washington, DC, USA, 2021, \r\n                                    pp. 1-8, doi: 10.1109\/PAINE54418.2021.9707712<\/br>\r\n\r\n                                <\/td>\r\n                                <td id=\"bibtexCitation\">\r\n                                    @INPROCEEDINGS{9707712, \r\n                                        author={Ghosh, Shajib and Sathiaseelan, Mukhil Azhagan Mallaiyan and Asadizanjani, Navid}, \r\n                                      \r\n                                        booktitle={2021 IEEE Physical Assurance and Inspection of Electronics (PAINE)},  \r\n                                      \r\n                                        title={Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey},  \r\n                                      \r\n                                        year={2021}, \r\n                                      \r\n                                        volume={}, \r\n                                      \r\n                                        number={}, \r\n                                      \r\n                                        pages={1-8}, \r\n                                      \r\n                                        doi={10.1109\/PAINE54418.2021.9707712}} \r\n                                <\/td>\r\n                            <\/tr>\r\n                        <\/table>\r\n                        <!-- Action Buttons for copying and downloading citations -->\r\n                        <div class=\"cite-action-buttons\">\r\n                            <button 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It highlights the significance of accurately recognizing component markings and reference designators for effective automatic Bill [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-425","page","type-page","status-publish","hentry"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/pages\/425","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/comments?post=425"}],"version-history":[{"count":79,"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/pages\/425\/revisions"}],"predecessor-version":[{"id":1729,"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/pages\/425\/revisions\/1729"}],"wp:attachment":[{"href":"https:\/\/physicaldb.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/media?parent=425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}