CVs Segmentation and Analysis using Image Processing and NLP techniques
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Recruitment work in the IT sector has recently increased due the large number of CVs being submitted to companies for hiring purposes. It becomes essential to use technological advancements to assist human resource experts analyzing these large numbers of CVs in order to reduce time and effort. Current related systems try to blindly find matches between the content of CVs and job posts without giving adequate attention to the semantic structure of CVS. In this paper, we propose a system to analyze CVs and to convert them into structured format using techniques from image processing and NLP. First, we conduct image processing segmentation to split CVs into informative pieces that are syntactically and semantically correlated regardless of their formats. Then, supervised machine learning is utilized to classify each segment into categories to support CV-job matching. Extensive experimental evaluation have beed conduct to validate both the segmentation and classification processes.
Image Processing, CVs Segmentation.