{"id":24807,"date":"2026-08-11T12:49:40","date_gmt":"2026-08-11T12:49:40","guid":{"rendered":"https:\/\/recruiterflow.com\/glossary\/?p=24807"},"modified":"2026-08-11T12:49:40","modified_gmt":"2026-08-11T12:49:40","slug":"resume-parsing","status":"publish","type":"post","link":"https:\/\/recruiterflow.com\/glossary\/resume-parsing\/","title":{"rendered":"What Is Resume Parsing?"},"content":{"rendered":"<p>Resume parsing is the automated process of extracting information from a resume or CV and converting it into structured data that recruiting software can store, search, filter, and reuse.<\/p>\n<p>A resume parser can identify information such as a candidate&#8217;s name, contact details, location, work history, education, skills, certifications, and profile links.<\/p>\n<p>Instead of recruiters manually copying information from every resume into an <a href=\"https:\/\/recruiterflow.com\/glossary\/applicant-tracking-system\/\">applicant tracking system<\/a>, resume parsing turns an unstructured document into searchable candidate data automatically.<\/p>\n<p>A parser does <strong>not<\/strong> decide whether a candidate is qualified, rank them against a job, or rewrite their resume for client presentation. Those are separate screening, <a href=\"https:\/\/recruiterflow.com\/glossary\/candidate-matching\/\">candidate matching<\/a>, and resume-formatting processes.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Resume_parsing_at_a_glance\"><\/span>Resume parsing at a glance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Resume parsing typically:<\/p>\n<ul>\n<li>Reads resume or CV content and identifies candidate information<\/li>\n<li>Maps extracted information into ATS or recruitment CRM fields<\/li>\n<li>Reduces manual data entry when creating candidate records<\/li>\n<li>Makes candidate information searchable and filterable<\/li>\n<li>Supports candidate matching, segmentation, workflows, and reporting<\/li>\n<li>Works separately from resume screening and candidate ranking<\/li>\n<li>May require recruiter review when documents are scanned, ambiguous, protected, or unusually formatted<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"How_does_resume_parsing_work\"><\/span>How does resume parsing work?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A resume parser generally follows six steps.<\/p>\n<h3>1. Ingest the resume<\/h3>\n<p>The parser first receives the candidate&#8217;s resume.<\/p>\n<p>A resume might enter the recruiting system through:<\/p>\n<ul>\n<li>A direct file upload<\/li>\n<li>An email attachment<\/li>\n<li>A job application<\/li>\n<li>A browser extension<\/li>\n<li>An integration<\/li>\n<li>An API<\/li>\n<li>A job maildrop<\/li>\n<\/ul>\n<p>The system then identifies the file type and determines whether the document contains readable text.<\/p>\n<h3>2. Extract the text<\/h3>\n<p>Text-based PDFs and word-processing files generally expose their text directly.<\/p>\n<p>Scanned resumes are different. Because they may only contain an image of the document, they usually require <strong>optical character recognition (OCR)<\/strong> to convert the image into machine-readable text before parsing can occur.<\/p>\n<p>Password-protected, corrupted, or unsupported files can also prevent successful extraction.<\/p>\n<h3>3. Identify resume sections and entities<\/h3>\n<p>Once the text is available, the parser identifies likely resume sections such as:<\/p>\n<ul>\n<li>Work experience<\/li>\n<li>Education<\/li>\n<li>Skills<\/li>\n<li>Certifications<\/li>\n<li>Contact information<\/li>\n<\/ul>\n<p>It then identifies individual entities within those sections, including:<\/p>\n<ul>\n<li>Employers<\/li>\n<li>Job titles<\/li>\n<li>Employment dates<\/li>\n<li>Educational institutions<\/li>\n<li>Qualifications<\/li>\n<li>Locations<\/li>\n<li>Email addresses<\/li>\n<li>Phone numbers<\/li>\n<\/ul>\n<h3>4. Normalize the information<\/h3>\n<p>Extracted information is then converted into standardized recruiting fields.<\/p>\n<p>For example, a parser may normalize:<\/p>\n<ul>\n<li>Dates<\/li>\n<li>Phone-number formats<\/li>\n<li>Locations<\/li>\n<li>Job titles<\/li>\n<li>Skill labels<\/li>\n<li>Employer names<\/li>\n<\/ul>\n<p>Normalization makes candidate records easier to search, filter, compare, and report on.<\/p>\n<h3>5. Create or update the candidate record<\/h3>\n<p>The structured information can then populate a new or existing candidate profile inside an ATS or recruitment CRM.<\/p>\n<p>Recruiting systems may also check identifying information such as:<\/p>\n<ul>\n<li>Email address<\/li>\n<li>Phone number<\/li>\n<li>LinkedIn or profile URL<\/li>\n<li>Candidate name<\/li>\n<li>Current employer<\/li>\n<\/ul>\n<p>This can help determine whether an existing candidate record already exists.<\/p>\n<p>Recruiters should still be able to review and correct parsed information when required.<\/p>\n<h3>6. Make the resume data usable<\/h3>\n<p>Once resume information is converted into structured fields, it can support other recruiting workflows such as:<\/p>\n<ul>\n<li>Candidate search<\/li>\n<li><a href=\"https:\/\/recruiterflow.com\/glossary\/candidate-matching\/\">Candidate matching<\/a><\/li>\n<li>Job pipelines<\/li>\n<li>Candidate segmentation<\/li>\n<li>Recruiting automations<\/li>\n<li>Communication<\/li>\n<li>Reporting<\/li>\n<li><a href=\"https:\/\/recruiterflow.com\/glossary\/candidate-rediscovery\/\">Candidate rediscovery<\/a><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"What_information_can_a_resume_parser_extract\"><\/span>What information can a resume parser extract?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The exact fields depend on the resume parser and source document, but common examples include:<\/p>\n<table>\n<thead>\n<tr>\n<th>Data group<\/th>\n<th>Example fields<\/th>\n<th>Common parsing challenge<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Identity and contact<\/td>\n<td>Name, email, phone, address, location, profile URL<\/td>\n<td>Headers, icons, multiple numbers, outdated details<\/td>\n<\/tr>\n<tr>\n<td>Employment<\/td>\n<td>Employer, title, start date, end date, description, current status<\/td>\n<td>Promotions, overlapping roles, consulting, missing dates<\/td>\n<\/tr>\n<tr>\n<td>Education<\/td>\n<td>Institution, degree, subject, dates, grade<\/td>\n<td>Abbreviations, international formats, incomplete entries<\/td>\n<\/tr>\n<tr>\n<td>Skills<\/td>\n<td>Technical, functional, language, tool, and domain skills<\/td>\n<td>Synonyms, proficiency, context, keyword stuffing<\/td>\n<\/tr>\n<tr>\n<td>Credentials<\/td>\n<td>Certifications, licences, memberships, clearances<\/td>\n<td>Expiry dates, issuing bodies, similar names<\/td>\n<\/tr>\n<tr>\n<td>Preferences or eligibility<\/td>\n<td>Location, work authorization, availability, compensation<\/td>\n<td>Information may be missing, outdated, or unsuitable for inference<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The original resume should also remain available alongside the structured candidate record. Recruiters may need to refer back to the source when extracted information is incomplete or disputed.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Resume_parsing_example\"><\/span>Resume parsing example<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Imagine a recruitment agency receives <strong>60 resumes for a specialist engineering role<\/strong>.<\/p>\n<p>Without resume parsing, recruiters may need to manually create 60 candidate records and enter details such as:<\/p>\n<ul>\n<li>Names<\/li>\n<li>Email addresses<\/li>\n<li>Phone numbers<\/li>\n<li>Employers<\/li>\n<li>Job titles<\/li>\n<li>Employment dates<\/li>\n<li>Qualifications<\/li>\n<\/ul>\n<p>With resume parsing, the agency can send those resumes into its ATS or recruitment CRM and automatically create structured candidate records.<\/p>\n<p>For example, the resumes might be sent to a job&#8217;s maildrop. Each resume is parsed and the resulting candidate is added to the relevant job.<\/p>\n<p>Recruiters can then review exceptions.<\/p>\n<p>One resume might be missing an email address. Another could use a two-column design that causes employment dates to be associated with the wrong company. A scanned PDF may require OCR or a text-based version of the resume.<\/p>\n<p>Once reviewed, recruiters can search the imported candidates by employer, title, skill, location, certification, and other structured fields.<\/p>\n<p>The parser accelerates data entry, while the original resume and recruiter review remain part of the process.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Resume_parsing_vs_resume_screening_vs_candidate_matching\"><\/span>Resume parsing vs. resume screening vs. candidate matching<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Resume parsing is often confused with several other recruiting processes.<\/p>\n<table>\n<thead>\n<tr>\n<th>Concept<\/th>\n<th>Main purpose<\/th>\n<th>Main distinction<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Resume parsing<\/strong><\/td>\n<td>Extract and structure resume information<\/td>\n<td>Creates searchable candidate fields<\/td>\n<\/tr>\n<tr>\n<td><strong>Resume screening<\/strong><\/td>\n<td>Evaluate candidate evidence against job criteria<\/td>\n<td>Assesses qualifications or fit<\/td>\n<\/tr>\n<tr>\n<td><strong>Candidate matching<\/strong><\/td>\n<td>Compare a candidate profile against a job<\/td>\n<td>Produces relevance or match signals<\/td>\n<\/tr>\n<tr>\n<td><strong>Resume formatting<\/strong><\/td>\n<td>Rebuild a resume using a consistent layout<\/td>\n<td>Changes how the resume is presented<\/td>\n<\/tr>\n<tr>\n<td><strong>OCR<\/strong><\/td>\n<td>Convert images of text into machine-readable text<\/td>\n<td>Makes scanned content available for parsing<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The key difference is that <strong>resume parsing is primarily an input and data-structuring process<\/strong>.<\/p>\n<p>Screening and matching may use parsed information later, but those processes evaluate the candidate against additional criteria or context.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_does_resume_parsing_work_with_an_ATS\"><\/span>How does resume parsing work with an ATS?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>An <a href=\"https:\/\/recruiterflow.com\/glossary\/applicant-tracking-system\/\">applicant tracking system<\/a> can use parsed resume data to:<\/p>\n<ul>\n<li>Create candidate profiles<\/li>\n<li>Add candidates to job pipelines<\/li>\n<li>Make candidate databases searchable<\/li>\n<li>Reduce duplicate data entry<\/li>\n<li>Populate reports<\/li>\n<li>Support candidate matching<\/li>\n<\/ul>\n<p>A recruitment CRM can use the same structured information for longer-term workflows such as:<\/p>\n<ul>\n<li>Candidate relationship management<\/li>\n<li>Segmentation<\/li>\n<li>Nurturing<\/li>\n<li>Talent mapping<\/li>\n<li>Candidate rediscovery<\/li>\n<\/ul>\n<p>The value increases when parsed fields remain connected to other candidate information, including resumes, emails, notes, calls, submissions, and placements.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_evaluate_resume_parsing_accuracy\"><\/span>How to evaluate resume parsing accuracy<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A good resume parser should not be evaluated using a single accuracy score.<\/p>\n<p>There are several factors recruitment agencies should test.<\/p>\n<h3>Field accuracy<\/h3>\n<p>Check whether the information populated by the parser matches the information in the original resume.<\/p>\n<p>Evaluate fields independently, including:<\/p>\n<ul>\n<li>Names<\/li>\n<li>Email addresses<\/li>\n<li>Phone numbers<\/li>\n<li>Employers<\/li>\n<li>Job titles<\/li>\n<li>Dates<\/li>\n<li>Education<\/li>\n<li>Skills<\/li>\n<li>Credentials<\/li>\n<\/ul>\n<p><strong>Field accuracy % = Correctly parsed populated fields \u00f7 Parsed populated fields reviewed \u00d7 100<\/strong><\/p>\n<h3>Field completeness<\/h3>\n<p>Accuracy alone does not tell you whether the parser captured everything that was present.<\/p>\n<p>Completeness measures how much expected information was successfully extracted.<\/p>\n<p><strong>Field completeness % = Correctly captured expected fields \u00f7 Expected fields present in source \u00d7 100<\/strong><\/p>\n<p>Accuracy and completeness measure different things.<\/p>\n<p>A parser might achieve high accuracy by leaving difficult fields blank. Another parser might populate more fields but introduce additional errors.<\/p>\n<h3>Record usability<\/h3>\n<p>A technically correct extraction is not automatically a useful candidate record.<\/p>\n<p>Test whether the resulting data works properly for:<\/p>\n<ul>\n<li>Candidate search<\/li>\n<li>Candidate matching<\/li>\n<li>Workflows<\/li>\n<li>Reporting<\/li>\n<li>Segmentation<\/li>\n<li>Duplicate handling<\/li>\n<\/ul>\n<h3>Correction effort<\/h3>\n<p>Measure how long recruiters spend reviewing and correcting each parsed resume.<\/p>\n<p>The operational benefit of resume parsing comes from reducing manual work, not simply generating a high parsing score.<\/p>\n<h3>Cohort coverage<\/h3>\n<p>Do not test a resume parser using only a small set of clean, standardized resumes.<\/p>\n<p>Use documents that reflect the agency&#8217;s actual candidate database, including different:<\/p>\n<ul>\n<li>Languages<\/li>\n<li>Regions<\/li>\n<li>Seniority levels<\/li>\n<li>Industries<\/li>\n<li>File types<\/li>\n<li>Resume layouts<\/li>\n<li>Career histories<\/li>\n<li>Candidate sources<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Common_resume_parsing_errors\"><\/span>Common resume parsing errors<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Resume parsing errors often occur because resumes are highly inconsistent documents.<\/p>\n<h3>Scanned or image-based resumes<\/h3>\n<p>A resume may look completely readable to a person while containing no machine-readable text.<\/p>\n<p>In these cases, OCR or a new text-based version of the resume may be required.<\/p>\n<h3>Multi-column or highly designed resumes<\/h3>\n<p>Tables, sidebars, icons, text boxes, and multiple columns can alter the document&#8217;s reading order.<\/p>\n<p>For example, a date displayed next to one employer visually may be associated with another employer during extraction.<\/p>\n<h3>Ambiguous employment dates<\/h3>\n<p>Promotions, overlapping roles, consulting engagements, concurrent positions, and missing months can make career timelines difficult to interpret.<\/p>\n<h3>Incorrect section detection<\/h3>\n<p>Projects, publications, volunteer work, credentials, and certifications may occasionally be interpreted as employment or education.<\/p>\n<h3>Unsupported language or file type<\/h3>\n<p>Resume-parsing coverage varies between recruiting platforms.<\/p>\n<p>Recruitment agencies should test the languages and document formats that actually appear in their candidate database.<\/p>\n<h3>Duplicate candidate creation<\/h3>\n<p>Candidate duplicates can occur when identifying information is inconsistent.<\/p>\n<p>Common causes include:<\/p>\n<ul>\n<li>Different email addresses<\/li>\n<li>Missing LinkedIn URLs<\/li>\n<li>Variations in candidate names<\/li>\n<li>Missing phone numbers<\/li>\n<li>Shared or outdated contact information<\/li>\n<\/ul>\n<p>Resume parsing should therefore be evaluated alongside a system&#8217;s duplicate-detection process.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_AI_is_changing_resume_parsing\"><\/span>How AI is changing resume parsing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Traditional resume parsers commonly rely on rules, statistical models, or a combination of techniques to identify structured information.<\/p>\n<p>Modern systems can also use machine learning and language models to recognize:<\/p>\n<ul>\n<li>Resume sections<\/li>\n<li>Entities<\/li>\n<li>Relationships between fields<\/li>\n<li>Skills<\/li>\n<li>Career chronology<\/li>\n<li>Information across less standardized layouts<\/li>\n<\/ul>\n<p>However, AI-based extraction can still misclassify information or infer something that the resume never explicitly states.<\/p>\n<p>Important fields should therefore remain traceable to their source where practical, particularly when the information will influence candidate assessment or client submissions.<\/p>\n<p>Resume parsing should also be distinguished from <strong>generative resume summarization<\/strong>.<\/p>\n<p>A summary interprets and compresses resume information. Parsing creates stable, structured fields that recruiting software can use for search, workflows, matching, and reporting.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_resume_parsing_works_in_Recruiterflow\"><\/span>How resume parsing works in Recruiterflow<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Recruiterflow is an AI-native ATS and recruitment CRM built for staffing, contingent, retained, and executive-search firms.<\/p>\n<p>Resume parsing helps turn candidate resumes into structured profiles that recruiters can use throughout their workflows.<\/p>\n<p>Recruiterflow supports several candidate-ingestion workflows, including:<\/p>\n<ul>\n<li>Resume uploads<\/li>\n<li>Job maildrops<\/li>\n<li>Email attachments<\/li>\n<li>Bulk candidate imports<\/li>\n<li>The Recruiterflow Chrome extension<\/li>\n<\/ul>\n<p>Recruiters can then review candidate information inside Recruiterflow.<\/p>\n<p>For troubleshooting parsing issues, see the <a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/7996760-my-resumes-are-not-being-parsed\">Recruiterflow resume parsing guide<\/a>.<\/p>\n<h3>Resume parsing vs. Recruiterflow&#8217;s CRM Update Agent<\/h3>\n<p>Resume parsing and Recruiterflow&#8217;s CRM Update Agent serve different purposes.<\/p>\n<p><strong>Resume parsing<\/strong> primarily converts resume information into structured candidate fields when candidate information enters the system.<\/p>\n<p>The <strong>CRM Update Agent<\/strong> can review information from sources such as files, notes, emails, calls, and other activities and suggest changes to missing or outdated candidate information.<\/p>\n<p>Recruiters can then review the suggestions before updating the profile.<\/p>\n<p>Learn more about <a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/12442136-what-are-crm-update-agents\">Recruiterflow&#8217;s CRM Update Agents<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Resume_parsing_checklist_for_recruitment_agencies\"><\/span>Resume parsing checklist for recruitment agencies<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Before relying on resume parsing at scale:<\/p>\n<ul>\n<li>Test resumes from your actual candidate population<\/li>\n<li>Include text PDFs, Word documents, scans, columns, and unusual layouts<\/li>\n<li>Measure field accuracy and field completeness separately<\/li>\n<li>Check employment dates and employer-title relationships carefully<\/li>\n<li>Confirm supported languages and file types<\/li>\n<li>Test duplicate-detection behaviour<\/li>\n<li>Preserve the original resume with the candidate record<\/li>\n<li>Measure recruiter correction time<\/li>\n<li>Test whether parsed information improves downstream search and matching<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Frequently_asked_questions_about_resume_parsing\"><\/span>Frequently asked questions about resume parsing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is resume parsing the same as resume screening?<\/h3>\n<p>No.<\/p>\n<p><strong>Resume parsing extracts and structures information from a resume. Resume screening evaluates a candidate&#8217;s evidence against job requirements or other selection criteria.<\/strong><\/p>\n<h3>Can a resume parser read a scanned PDF?<\/h3>\n<p>It depends on the product.<\/p>\n<p>A scanned PDF may contain an image rather than machine-readable text. OCR or another process that creates a readable text layer may therefore be required before normal resume parsing can occur.<\/p>\n<h3>Why does parsed resume data need to be reviewed?<\/h3>\n<p>Resumes vary considerably in layout, chronology, language, and level of detail.<\/p>\n<p>Reviewing parsed information can identify problems such as:<\/p>\n<ul>\n<li>Missing fields<\/li>\n<li>Incorrect employment dates<\/li>\n<li>Employer-title mismatches<\/li>\n<li>Incorrectly detected sections<\/li>\n<li>Duplicate candidate records<\/li>\n<\/ul>\n<h3>What is the difference between CV parsing and resume parsing?<\/h3>\n<p>In practice, <strong>CV parsing and resume parsing generally refer to the same underlying process<\/strong>: extracting information from a candidate document and converting it into structured recruiting data.<\/p>\n<p>The term used usually depends on the region and whether the candidate document is referred to as a CV or resume.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Related_recruiting_terms\"><\/span>Related recruiting terms<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Continue exploring the Recruiterflow recruiting glossary:<\/p>\n<ul>\n<li><a href=\"https:\/\/recruiterflow.com\/glossary\/applicant-tracking-system\/\">Applicant tracking system (ATS)<\/a><\/li>\n<li><a href=\"https:\/\/recruiterflow.com\/glossary\/candidate-matching\/\">Candidate matching<\/a><\/li>\n<li><a href=\"https:\/\/recruiterflow.com\/glossary\/resume-screening\/\">Resume screening<\/a><\/li>\n<li><a href=\"https:\/\/recruiterflow.com\/glossary\/candidate-rediscovery\/\">Candidate rediscovery<\/a><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"More_Recruiterflow_resources\"><\/span>More Recruiterflow resources<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/7996760-my-resumes-are-not-being-parsed\">Troubleshoot resume parsing<\/a><\/li>\n<li><a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/1241386-uploading-candidates-in-bulk\">Upload candidates in bulk<\/a><\/li>\n<li><a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/2679397-sourcing-candidates-from-linkedin-using-the-recruiterflow-chrome-extension\">Source candidates with the Recruiterflow Chrome extension<\/a><\/li>\n<li><a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/12442136-what-are-crm-update-agents\">Learn about Recruiterflow CRM Update Agents<\/a><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Turn_resumes_into_usable_recruiting_data\"><\/span>Turn resumes into usable recruiting data<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Resume parsing is most valuable when the extracted information becomes useful throughout the recruiting workflow \u2014 from candidate creation and search to matching, rediscovery, reporting, and database management.<\/p>\n<p>Before relying on any parser at scale, test it against a realistic sample of your candidate database and measure both <strong>extraction quality and the time recruiters spend correcting the results<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Resume parsing is the automated process of extracting information from a resume or CV and converting it into structured data that recruiting software can store, search, filter, and reuse. A resume parser can identify information such as a candidate&#8217;s name, contact details, location, work history, education, skills, certifications, and profile links. Instead of recruiters manually <a href=\"https:\/\/recruiterflow.com\/glossary\/resume-parsing\/\" class=\"more-link\">&#8230;<span class=\"screen-reader-text\">  What Is Resume Parsing?<\/span><\/a><\/p>\n","protected":false},"author":31,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[115],"tags":[],"class_list":["post-24807","post","type-post","status-publish","format-standard","hentry","category-recruitment"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.7 (Yoast SEO v28.1) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>What Is Resume Parsing? - Recruiterflow Glossary<\/title>\n<meta name=\"description\" content=\"Resume parsing is the automated process of extracting information from a resume or CV and converting it into structured data that recruiting software can store, search, filter, and reuse.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/recruiterflow.com\/glossary\/resume-parsing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What Is Resume Parsing?\" \/>\n<meta property=\"og:description\" content=\"Resume parsing is the automated process of extracting information from a resume or CV and converting it into structured data that recruiting software can store, search, filter, and reuse.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/recruiterflow.com\/glossary\/resume-parsing\/\" \/>\n<meta property=\"og:site_name\" content=\"Recruiterflow Glossary\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/recruiterflow\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-11T12:49:40+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/recruiterflow.com\/glossary\/wp-content\/uploads\/2026\/07\/Group-6754-e1683281335979.png.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"672\" \/>\n\t<meta property=\"og:image:height\" content=\"140\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Abhishek Sharma\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@recruiterflow\" \/>\n<meta name=\"twitter:site\" content=\"@recruiterflow\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Abhishek Sharma\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/resume-parsing\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/resume-parsing\\\/\"},\"author\":{\"name\":\"Abhishek Sharma\",\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/#\\\/schema\\\/person\\\/e49b0f8dc111400d65599717285b3172\"},\"headline\":\"What Is Resume Parsing?\",\"datePublished\":\"2026-08-11T12:49:40+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/resume-parsing\\\/\"},\"wordCount\":2071,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/#organization\"},\"articleSection\":[\"Recruitment\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/resume-parsing\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/resume-parsing\\\/\",\"url\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/resume-parsing\\\/\",\"name\":\"What Is Resume Parsing? 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