Qu'est-ce que CV Parsing ?

Definition

CV parsing is the automated process of reading an unstructured CV (typically a PDF or Word document) and extracting information into structured, searchable data fields — name, contact details, work history (employer, title, dates, description), education, skills, languages, certifications. The parsed data is then stored in an ATS or used to power candidate matching.

In practice

Modern CV parsers use NLP (natural language processing) and machine learning to interpret and structure free-text CV content. Leading parsers (Sovren, Textkernel, HireAbility, Daxtra) achieve high accuracy for standard CV formats in major Western languages. Accuracy degrades with: non-standard layouts, creative designs, tables and graphics, unusual job title conventions, abbreviations, and languages outside the parser's training data. For Belgian multilingual CVs (French/Dutch/English sections), language detection and parsing accuracy requires specific multilingual training. From a candidate experience perspective, poorly parsed CVs create frustration — data appearing in wrong fields, names misspelled, dates transposed. ATS systems that rely heavily on parsed data without allowing candidate correction introduce systematic errors into the hiring process. Best practice allows candidates to review and correct parsed data before submission.

Key takeaway

CV parsing dramatically reduces manual data entry but is not perfectly accurate — always provide candidates the ability to review and correct parsed data to avoid losing good candidates to parsing errors.