Intelligent Resume Parser

What It Solves

Resume files are often stored as PDF or DOC documents, and job title, skills, certifications, and project history still require manual extraction. Without a consistent taxonomy, the resulting Excel roster can drift in skill labels, field formats, and traceability to the original file.

How It Works

• It first confirms the working directory and checks dependencies. The scripts read the resume library via command-line arguments and do not modify source files.
• extract_all_formats.py converts PDF and DOC formats into raw_*.txt files.
• generate_excel.py invokes parse_resume.py and outputs a styled Excel workbook with 15 columns and clickable links.
• Skills are mapped to Cloud Network, Data, Security, Platform, AI, and Video categories, up to 10 items. Project history is structured as project-role-responsibility, up to 5 items.
• The Excel output freezes the first row, alternates row colors, wraps long text, and includes an [Open] filename.pdf link in the final column.
• It backs up the previous Excel file before generation and saves with an _old_ prefix if the file is locked.

Boundaries and Notes

• Scheduling is left blank: current schedule and availability are intended for manual entry.
• Standardization first: skill extraction relies on a fixed keyword set, which is better for a consistent roster than free-form semantic analysis.
• Incremental updates: new resumes should be added to the resume library, then extraction and generation should be rerun; the result overwrites the previous data.
• Format handling: PDF Chinese parsing prefers pdfplumber; if DOC parsing fails, olefile can read the OLE stream.