AI-Powered Smart Resume Builder — 50+ professional templates, PDF/image parsing, AI optimization, JD match analysis, multi-format export. Open source & free, one-click Docker deployment.

The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score

This site uses Lever's resume parsing API to parse resumes

This is a project aiming to optimize the recruitment process. It integrates an advanced Applicant Tracking System with Google Gemini Pro, streamlining resume parsing, keyword matching, and candidate evaluation for an efficient end-to-end solution in talent acquisition.

Parsing resumes in a PDF format from linkedIn

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

Our project on GitHub offers a versatile resume parsing tool. It provides two methods for extracting information: a straightforward approach using regular expressions and a more advanced method using SpaCy's natural language processing capabilities. Choose the method that suits your needs and streamline your resume processing tasks effectively.

CVparser is software for parsing or extracting data out of CV/resumes.

A resume/CV generator, parsing information from YAML file to generate a static website which you can deploy on the Github Pages. Exactly like resume-version Hexo.

A ruby library used for parsing resumes using the Sovren resume parser.

An AI-powered suite to automate job scraping, resume parsing, job-to-resume scoring, and application tracking, designed to run via GitHub Actions.

This is an end to end machine learning project served on Flask for parsing and ranking candidates based on similarity scores between resumes and job description.

This Repository contains AI Resume Analyzer that utilizes PDF parsing, database management, SQL-Python integration, and data extraction from PDFs. It offers skill recommendations and suggests videos and lectures for skill enhancement, aiming to enhance resume quality and job prospects.

Key features: - Automated AI-driven interviews - Resume analysis and parsing - Real-time video conferencing - Structured evaluation of candidates - Customizable job positions and interview questions

Resume Parsing app to extract information using AI

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An automated Hybrid Resume NER based on Rule-Based model, Machine Learning Model, and Transformer model

本项目基于Python的Flask框架开发，旨在实现用户上传简历后，系统能够自动解析简历内容并智能匹配合适的岗位。该系统通过调用公开接口，对上传的简历进行解析，提取关键信息，并根据这些信息自动匹配最适合的岗位，提供个性化推荐。

Develop a simple yet accurate Resume parsing model using pdfminer and web-app using streamlit to match candidate profiles with suitable job offers, and a chatbot to make cost-effective and efficient hiring solutions for Moroccan startups and companies using llm gpt-3.5-turbo.

An advanced LLM-powered application offering features like Resume Parsing, OCR, and interactive LLM chat. Built using open-source Llama 3, Ollama, LM Studio, Flask, and more, it hosts LLMs locally for high-performance and secure processing.

Resume Insights is an advanced resume parsing and analysis tool that leverages the power of LlamaIndex, Gemini, and Streamlit to extract key information from PDF resumes and present it in an intuitive, interactive format.

🚀 An advanced Resume Tracking System leveraging Large Language Models (LLM) and GenAI for streamlined resume parsing and analysis. Deployed on Streamlit for a seamless user experience.

AI-powered assistant for EspoCRM with resume parsing and natural language interface

Python client for MagicalAPI: resume parsing, resume scoring, LinkedIn profile and company data extraction for hiring and business insights.

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Resume parsing website

Smart AI career assistant - resume parsing, skill gap analysis, and personalized guidance for students and professionals.

A machine learning and NLP Implementation in parsing and classifying resumes.

Artificial Intelligence has recently been used to automate resume parsing which deciphers various resumes using an NLP (Natural Language Processing) engine. It is important for recruiters as it may locate excellent candidates with a perfect match in qualifications, talents, and experience in a similar industry.

Resume Parsing | Custom Build Model