Resume Parsing Flowchart

Explanation of the components: Resume Bank: Database containing resumes of potential candidates. PDF Text Extraction: Utilizes PDF parsing libraries like pdfminer to extract text content from resume files. SpaCy Setup: Installation of SpaCy and loading necessary language models. Name Extraction: Uses SpaCy's linguistic capabilities and Matcher module to extract names from resume text. Contact Details: Employs regular expressions to extract contact numbers from resume text. Email Extraction: Utilizes regular expressions to identify and extract email addresses from resume text. Skills Extraction: Creates a predefined list of skills and uses SpaCy's linguistic capabilities to match and extract skills from the resume text. Education: Defines a set of education keywords or patterns and uses regular expressions to extract education details from the resume text. Resume Parser: Combines individual extraction functions to create a comprehensive resume parser. Structured Output: Outputs the extracted information in a structured format for further processing or analysis. This architecture provides a modular and organized approach to resume parsing using SpaCy, allowing for easy customization and expansion based on specific requirements. It enables the extraction of key information from resumes and organizes it into a structured output for efficient candidate evaluation.