The objective of the project is to create a Resume Scoring algorithm using Natural Language Processing. The algorithm will parse resumes one by one and will create a Candidate Profile based on the skills mentioned in the resume. A corpus is created using Sketch Engine, Wikipedia pages for various required skills (example : Machine Learning, Data Science, Software developer, Programming) are given as input, Sketch Engine creates a corpus from the useful data available on the given links. Word Embeddings are created from the corpus and these are used to match the skills in the candidate resume with the required skills. Finally, the candidate profile is built and plotted as a bar graph for better visualization.

Created a hybrid content-based & segmentation-based technique for resume parsing with unrivaled level of accuracy & efficiency. Provided resume feedback about skills, vocabulary & third-party interpretation, to help job seeker for creating compelling resume

Repo to win Deloitte Hackathon 2019

The Smart Resume Analyzer is an AI-based tool designed to automate the resume screening process. It parses resumes, extracts key information (skills, experience, education), and compares them to job descriptions using Natural Language Processing (NLP). The system scores and ranks candidates based on job fit and provides feedback for improvement.

A resume parser created using regex based techniques. It extracts phone number, email id, educational qualification, experience and finds relevance of resume to the skill set required for job.

Talent Scout AI is a resume screening system that uses Google Gemini to parse resumes, evaluate skills, and rank candidates based on job-specific requirements all through a modern, easy-to-use HR dashboard.

An NLP-based Resume Parser that extracts key information such as personal details, skills, education, and experience from resumes. Designed to streamline recruitment processes by automating resume analysis.

TeamComp is an API that utilizes data to match consultants to projects based on their skills by parsing Resume skills as well as project keywords and then perform the matching.

NextSkill is an AI-powered career companion that analyzes resumes and interests to identify skill gaps and provide personalized upskilling roadmaps based on real-time job market data. Built with Java Spring Boot, PostgreSQL, and Next.js, it features advanced parsing, AI-driven recommendations, and gamified progress tracking.

Built NLP-based system to parse, embed, and rank resumes using Sentence Transformers, Implemented cosine similarity ranking and skill extraction, Deployed using Streamlit, Integrated explainability module for HR transparency

SimpleResumeParser is a lightweight resume parsing framework that extracts names, emails, and skills from PDF or DOCX resumes using a combination of LLMs (RAG), HuggingFace NER models, rules-based logic, and regex patterns. It’s designed with pluggable architecture for production-ready, structured data extraction.

HireSense is an AI-powered resume intelligence platform that goes beyond traditional ATS filtering by using semantic transformer models to deeply understand candidate profiles. It parses resumes to extract structured skills, technologies, and project experience based on real contextual meaning rather than keyword matching.

A Python-based resume parser that extracts key information (name, contact info, skills, experience, etc.) from PDF resumes and returns structured data.

Resume Parser is a Web Application that will act as a bridge between the recruiters and candidates. We are trying to provide an application where a candidate can easily search for relevant job postings and where a recruiter can easily search the suitable candidates for the job, based on their skills and profiles.

This project is a Python-based resume parser and grader that extracts skills, experience and projects from resumes. It then compares the resume against a job description to generate a match score, similar to ATS.

A Streamlit-based Resume Parser powered by LangChain and Google Gemini 2.5 Flash, capable of extracting structured resume information (Name, Skills, Education, Projects, Experience, etc.) from uploaded PDF, DOCX, or TXT files.

A comprehensive resume parser, capable of extracting detailed information from resumes in various formats (PDF, JPG, HTML, DOC, etc.). The parser should accurately classify text into distinct sections (e.g., education, work experience, skills) and sequence them based on dates, where available. Also

A Spring Boot–based backend project designed for modern recruitment management. The platform enables resume parsing, skill-based candidate matching, interview scheduling, and pipeline tracking.

Resume Analyzer is a Python-based AI tool that parses resumes, extracts key info, and matches them with job descriptions. It helps job seekers optimize resumes by highlighting skills and experience relevant to job roles using NLP and PDF parsing.

The objective of the project is to create a Resume Scoring algorithm using Natural Language Processing. The algorithm will parse resumes and will create a Candidate Profile based on the skills mentioned in the resume.

A Python-based resume parser application built with LangChain and Streamlit that extracts structured information from PDF, DOCX, and TXT resume files. The tool uses Google's Gemini AI model to parse resumes and extract key details like personal information, skills, education, experience, and projects into a clean JSON format.

End-to-end ML systems integrating semantic matching, JD/Resume parsing, Lightcast skill extraction, FAISS retrieval, and LLM-based interview evaluation with CEFR-aligned linguistic metrics.

This Resume Parser is a Python-based application that extracts key information from resumes in PDF format. Built with Tkinter for the GUI, it uses pdfplumber to read PDF files and regex patterns to identify and parse relevant sections such as name, email, phone number, skills, projects, and eligible sectors based on predefined categories.

Smart Resume Parser is a Streamlit-based app that extracts key information from resumes (PDF/DOCX), including contact details, skills, education, experience, and job roles like Intern or Developer. Built with Python, it uses regex and basic NLP to convert unstructured resume text into structured, recruiter-friendly data

Resume Ranking AI by Django is an AI-powered resume screening system that ranks resumes based on job descriptions. It uses Django, and Machine Learning to analyze skills, experience, and qualifications. Features include PDF/DOCX parsing, skill matching, and REST API support. Easily customizable and integrates with HR systems.

Parses resumes to extract technical keywords using regex. Matches extracted skills to job roles from a JSON-based database. Runs as a FastAPI microservice for easy integration with HR systems.

An automated resume screening system that parses PDF resumes, extracts key information, and ranks candidates based on job-specific criteria — similar to how modern ATS platforms operate. Built using Python, Tesseract OCR, NLP pipelines, and rule-based scoring to evaluate skills, experience, and relevance with high accuracy.

AI-Powered Resume Ranking System 🚀 A Java Spring Boot application that parses resumes, extracts skills, and ranks candidates based on keyword matching. Uses Apache Tika for text extraction, Trie data structure for skill lookup, and Priority Queue for ranking. Let me know if you need any tweaks! 😊

AI-powered Resume Scanner with OCR, PDF parsing, smart filtering, ML-based job matching, chatbot, and voice summary. Built using Python + Streamlit, it helps HRs filter candidates by skills, experience, education & more. Includes bulk email, resume preview, and bilingual voice summary.

CYGNUSA Elite-Hire is an AI-powered resume intelligence and candidate shortlisting platform that parses resumes, performs multi-layer skill matching, adaptive candidate scoring, and hiring analytics. It provides role-based evaluation, explainable hiring decisions, and recruiter-focused dashboards to improve screening accuracy and efficiency.