Resume Parser

Resume Parser — Extract Candidate Data Instantly

Resume Parser uses AI and NLP to extract structured data from resumes in any format. Names, contact details, skills, work history, education, certifications — all parsed with 95%+ accuracy and delivered in a clean, structured format ready for your ATS or database.

Process hundreds of resumes per minute. Eliminate manual data entry from your recruitment workflow and let your team focus on evaluating candidates, not typing their details.

Key Features

Multi-Format Support

Parse PDF, DOCX, DOC, RTF, TXT, and HTML resumes. Handles diverse layouts, designs, and international formats.

AI-Powered Extraction

NLP models trained on millions of resumes. Accurately extracts 40+ data fields including skills, experience levels, and education.

Skill Taxonomy Mapping

Maps extracted skills to your custom taxonomy or industry-standard classifications. Enables accurate candidate-to-job matching.

Batch Processing

Upload hundreds of resumes at once. API supports high-volume processing for job boards and staffing agencies.

ATS Integration

REST API and webhooks for seamless integration with Greenhouse, Lever, Workday, BambooHR, and custom ATS platforms.

Multilingual Support

Parse resumes in English, Hindi, and other major languages with localised field recognition and date formats.

Why Resume Parser

95%+ Accuracy

Continuous ML model improvement trained on diverse resume formats. Industry-leading extraction accuracy.

Sub-Second Processing

Parse a single resume in under 500ms. Batch mode handles 200+ resumes per minute.

Privacy Compliant

GDPR-ready data handling. Option to process on-premise with no data leaving your infrastructure.

Customisable Fields

Configure which fields to extract, add custom fields, and define output format to match your database schema.

Processing resumes manually?

Try Resume Parser free for 14 days. Parse up to 500 resumes with full functionality.

Start Free Trial

Built With

Python / NLP

TensorFlow

spaCy

REST API

Docker

PostgreSQL

React

AWS / On-Prem

Frequently Asked Questions

How accurate is the parsing?

95%+ across standard fields. Accuracy improves further with custom training on your resume corpus.

Can it handle non-standard resume formats?

Yes. ML models handle creative layouts and infographic resumes. Accuracy remains above 85% for heavily designed resumes.

What output formats are supported?

JSON, XML, and CSV. Custom output schemas available. Direct database insertion via API also supported.

How many resumes can it process per minute?

200+ resumes per minute in batch mode. Single resume parsing completes in under 500ms.

Does it support non-English resumes?

Yes. English, Hindi, and other major languages with localised field recognition and date format handling.

Can it extract skills and map them to our taxonomy?

Yes. Built-in skill taxonomy with 50,000+ skills. Custom taxonomy mapping available to match your job descriptions and internal classifications.

Is there an API for integration?

Yes. RESTful API with comprehensive documentation. SDKs available for Python, Node.js, Java, and C#. Webhook support for async processing.

How do you handle data privacy?

GDPR-compliant processing. On-premise deployment option available. Data is never used for training without explicit consent. Auto-deletion policies configurable.

Can it parse LinkedIn profiles?

Yes. LinkedIn profile URLs and exported PDFs can be parsed with the same accuracy as traditional resumes.

Does it detect duplicate candidates?

Yes. Fuzzy matching on name, email, phone, and skills to identify duplicate submissions across different resume versions.