How to parse resumes in Python (and when to level up to Affinda)

Even with today’s digital-first hiring platforms, resume parsing remains a real technical hurdle for recruitment technology providers. Product and engineering teams building ATSs, job boards or candidate-matching platforms continue to face hundreds – if not thousands – of resumes, all formatted differently and structured in their own unique way.

For platform developers, the goal is to eliminate this manual step for your customers, providing structured, high-quality data they can rely on.

In this article, we’ll explore how to parse resumes in Python, from quick do-it-yourself (DIY) methods to open-source libraries and AI solutions that handle resume data extraction at scale.

It’s designed for HR technology professionals and ATS developers who want to understand how Python can be used to extract data from resumes, and when it makes sense to move from DIY scripts to enterprise-grade automation.

Start with the basics: parsing resumes with Python

If you’re a developer, the most straightforward way to start is to write a simple Python resume parser using existing NLP libraries.

A basic approach looks like this:

1. Convert the document to plain text: use tools like pdfminer (for PDFs) and doc2text (for .docx files) to extract text
2. Run entity recognition: use spaCy or nltk for named entity recognition (NER) to find names, emails, phone numbers and educational qualifications
3. Match skill keywords: build a list of known skills and match them against the text using tokenization or regex rules

This DIY method helps you understand the logic behind resume parsing, but it’s rarely production-ready. A hand-built parser can handle some simple, text-based resumes, but it will struggle with:

• Unstructured layouts (columns, tables, creative designs)
• Variations in headings (such as ‘Strengths’ instead of ‘Skills’)
• Multilingual documents
• Contextual data like employment duration or skill proficiency

If you’re building a recruitment platform or ATS, the accuracy of your data directly shapes how well your customers can match candidates to the right jobs and stay compliant.

Explore open-source resume parser libraries in Python

If you don’t want to build everything from scratch, you can use open-source resume parser Python libraries.

For example, Omkar Pathak’s open-source parser on GitHub is a popular option. It’s a Django-based web app that can be installed with pip and launched locally, letting you upload and parse resumes through a simple interface.

However, open-source tools come with limitations:

• They don’t learn or adapt to new resume formats
• There’s no support for large-scale processing
• Accuracy and data coverage vary widely
• There’s limited or no ongoing maintenance

If you’re experimenting or learning about natural language processing, these projects are great practice. But for production-grade recruitment platforms, open-source parsers typically can’t deliver the scalability, maintenance or field coverage required for enterprise customers.

Upgrade to an intelligent resume parser built for scale

After experimenting with DIY and open-source tools, most teams run into the same challenges – particularly around accuracy, scaling and getting everything to integrate cleanly without any major system changes. While it’s possible to extract data from resumes using Python, achieving enterprise-grade accuracy and scalability usually requires a more advanced resume processing platform.

That’s when it’s time to consider an intelligent document processing solution, one designed to handle resumes with the precision, adaptability and consistency that Python alone can’t easily achieve.

Our resume parser is built for recruitment technology providers. It’s not a generic AI tool but a trained machine learning model (non-LLM) designed to deliver structured candidate data at enterprise scale.

Here’s how it works:

• Built for developers: Affinda offers ready-to-use Python libraries, plus SDKs for Java, TypeScript and C#
• Advanced NLP architecture: our trained machine learning models (not LLMs) extract more than 100 structured fields, from personal information and work history to skills and qualifications
• Context-aware accuracy: it understands synonyms, creative headings and variations (‘What I’m great at’ = skills)
• Seamless integration: use the Affinda API to connect directly with your ATS or HR software
• Continuous learning: our parser’s model memory allows it to adapt to new resume formats and customer use cases without full retraining

You’ll get structured JSON output with fields such as name, email, skills and experience, ready to feed directly into your database or ATS.

Avoid the cost and complexity of in-house research and development (R&D) and upkeep. With our API, you can offer reliable, high-accuracy resume parsing right away – and at enterprise scale. 

Why scaling resume parsing requires AI

Parsing a handful of resumes is one thing, but scaling it across thousands of documents per week is another challenge entirely.

Even the most sophisticated Python scripts can’t easily solve for:

• layout inconsistencies and image-based text
• tables and nested formatting
• non-standard or industry-specific terminologies
• continuous model retraining and data governance

Our resume parsing platform uses a trained machine learning architecture (non-LLM) enhanced with agentic AI and persistent model memory. This enables it to adapt instantly to new formats and data patterns.

For recruitment tech vendors and enterprise HR software providers, this translates into:

• 99%+ accuracy
• 10x faster processing
• 95% reduction in manual data entry

Bulk resume parsing for recruiters and ATS providers

When your goal is to process thousands of resumes at once, bulk resume parsing becomes essential.

Using our API, teams can upload large batches of resumes and receive structured data for every candidate automatically.

Here’s what that looks like in practice:

1. Upload all resumes in formats such as PDFs, Word docs or scanned images
2. Affinda’s AI reads layout, context and entities to identify and classify 100+ data points
3. Parsed data feeds directly into your ATS, CRM or talent management system for you to review

The result? Your customers see better and faster candidate insights, and your team can deliver a richer platform experience – without adding to their development overheads.

When to build vs buy your resume parser

Many HR tech teams are facing the same crossroad: should we build our own parser or buy a ready-made solution?

Here’s a quick comparison:

For most recruitment tech companies, using a proven parser means delivering value to customers sooner while giving your team more time to work on the parts of the platform that truly matter.

From DIY parsing to enterprise precision

Python gives you a great starting point for understanding how resume parsing works, but scaling it to professional standards is another story – and a story we know well.

If you’re a product or engineering lead at a recruitment technology company, your team’s time is better spent improving the customer experience – not maintaining brittle parsing code.

Affinda’s AI resume parser takes care of the hard parts, with easy integration, bulk processing and unmatched accuracy.

Start your free trial and see how quickly you can integrate our parser to your existing stack.