Automatic Summarization of Resumes with NER -> Evaluate resumes at a glance through Named Entity Recognition

Name entity recognition utilized for the Resume parser using NLP spacy tool in python

This project includes extracting useful information from resume using Named Entity Recognition and phrase matching in Spacy

This project includes extracting useful information from resume using Natural Language Processing using Named Entity Recognition and phrase matching in Spacy

A Streamlit-based NLP application for automated resume parsing and entity extraction. Utilizes spaCy for Named Entity Recognition (NER) to extract key information from PDF resumes. Displays extracted text and classified entities in a user-friendly web interface.

Project done as a part of internship at ThoughtRipples Technologies, in which we developed a 'Resume Analyser & Extractor' using Named Entity Recognition and Spacy in Natural Language Processing.

The Resume Parser project aims to build a resume parser using the spaCy library in Python. The parser will extract relevant information from a resume, such as personal details, work experience, education, and skills. The project uses the spaCy library for Named Entity Recognition (NER) tasks.

A dual-module NLP project exploring Part-of-Speech tagging and Named Entity Recognition. Achieved 93.6% accuracy in PoS tagging with a Bi-GRU model and implemented a production-ready spaCy pipeline to handle noisy, non-aligned entity annotations in resume data.

RecruitMatch AI is a GenAI-based application that automates resume screening for recruitment teams by intelligently ranking candidates against job descriptions. The system processes multiple resumes in PDF, DOCX, or TXT formats, extracting structured information through advanced NLP pipelines using spaCy for named entity recognition.