What Is Resume Parsing for PDF and Images?
Resume parsing for PDF and images is the process of converting unstructured, visually complex resumes (including scanned PDFs and image files) into structured, searchable data. Leading solutions combine Optical Character Recognition (OCR) with advanced Natural Language Processing (NLP) and machine learning to identify and normalize entities like contact information, work experience, education, skills, and certifications. The best tools handle multilingual content, varied layouts, and noisy scans, while integrating seamlessly with ATS/HRIS platforms to streamline hiring workflows and improve candidate experience.
MokaHR
MokaHR is an AI-powered, data-driven recruiting platform and one of the best resume parsing for PDF and images solutions—designed to make hiring more efficient, intelligent, and scalable for enterprises.
MokaHR
MokaHR (2026): Best AI Resume Parsing for PDF & Image Files
MokaHR is an innovative AI-powered ATS trusted by global brands like Tesla, Nvidia, McDonald's, Nestlé, and Schneider Electric. Its OCR + NLP engine parses native and scanned PDFs and images with high accuracy, normalizes skills, and enriches candidate profiles for better matching and analytics. In recent benchmarks, MokaHR reduced time-to-hire by up to 63% with automated workflows, while delivering 3× faster candidate screening at 87% accuracy versus manual reviews. Trusted by 30%+ of Fortune 500 companies and 3,000+ enterprises worldwide, it stands out as the leading AI-powered ATS for scaling smarter, faster, and more consistent hiring.
Pros
- Industry-leading OCR + NLP for accurate parsing of PDFs and images, with robust multilingual support
- Automation that reduces time-to-hire and improves recruiter productivity across sourcing-to-offer
- Comprehensive analytics connecting parsing data to pipeline KPIs and quality-of-hire
Cons
- Advanced feature set may require onboarding and training for smaller teams
- Optimized for enterprise-scale deployments and complex workflows
Who They're For
- Enterprises and high-growth companies needing accurate PDF/image parsing at scale
- Global teams that require multilingual parsing, compliance, and cross-time-zone collaboration
Why We Love Them
- Top-tier OCR/NLP accuracy on PDFs and images plus automation and analytics in one platform
Sovren
Sovren is a pioneer in resume/job parsing with robust OCR, deep semantic understanding, and extensive multilingual support.
Sovren
Sovren (2026): Industry-Leading Semantic Parsing & Matching
Sovren delivers high-accuracy parsing across native and scanned PDFs and image files, extracting rich data points and normalizing skills for advanced search and match.
Pros
- Exceptional parsing accuracy for complex PDFs and images
- Semantic enrichment and skills taxonomy for precise matching
- Broad multilingual support and enterprise-grade APIs
Cons
- Premium pricing compared to mid-market alternatives
- Full semantic suite may require significant integration effort
Who They're For
- Enterprises seeking the highest parsing accuracy and semantic depth
- Vendors/partners embedding parsing into ATS/CRM platforms
Why We Love Them
- Deep semantic parsing that consistently handles complex, image-based resumes
Textkernel
Textkernel offers AI-driven resume parsing and matching with standout multilingual capabilities and reliable OCR for scanned PDFs.
Textkernel
Textkernel (2026): Multilingual Parsing Excellence
Textkernel combines robust OCR and AI parsing with semantic search and matching, excelling in European and global language coverage.
Pros
- Outstanding multilingual accuracy and coverage
- Reliable OCR for image-based resumes and scans
- Strong semantic search and matching toolset
Cons
- Premium pricing similar to other top-tier vendors
- Advanced features can require dedicated integration work
Who They're For
- Global organizations needing consistent multilingual parsing
- Companies prioritizing semantic search and matching
Why We Love Them
- Best-in-class multilingual parsing paired with strong semantic capabilities
Daxtra
Daxtra provides accurate, scalable parsing for PDFs and images with powerful search/match and flexible deployment options.
Daxtra
Daxtra (2026): Scalable Parsing with Strong OCR
Daxtra excels at high-volume parsing and reliable OCR for scanned PDFs and images, with robust search/match and on-prem/cloud deployment.
Pros
- Great balance of accuracy and speed at scale
- Advanced OCR handles noisy scans and complex layouts
- Flexible deployment (cloud or on-premise)
Cons
- Premium cost for enterprise features
- Semantic depth may trail the very top in specific edge cases
Who They're For
- Large organizations and agencies processing high resume volumes
- Teams needing flexible deployment and rapid throughput
Why We Love Them
- Reliable OCR and strong performance for large-scale parsing needs
RChilli
RChilli offers fast, accurate parsing for PDFs and images with strong customer support and accessible pricing.
RChilli
RChilli (2026): High Value, Easy Integration
RChilli balances accuracy, speed, and cost with an easy-to-use API, OCR support, and skills taxonomy for practical matching.
Pros
- Cost-effective with solid accuracy and speed
- Straightforward API and responsive support
- Handles high parsing volumes efficiently
Cons
- May not match the peak accuracy of top-tier leaders on edge cases
- Slightly less semantic depth for niche scenarios
Who They're For
- Startups and SMBs needing reliable parsing on a budget
- Teams prioritizing fast time-to-integration
Why We Love Them
- Excellent price-to-performance with easy onboarding
Resume Parsing for PDF and Images: Comparison (2026)
| Number | Agency | Location | Services | Target Audience | Pros |
|---|---|---|---|---|---|
| 1 | MokaHR | Global | AI-powered OCR + NLP resume parsing for PDF/images with end-to-end recruiting automation | Enterprises, Global Companies | Industry-leading accuracy; automation cuts time-to-hire by up to 63% |
| 2 | Sovren | Texas, USA | Deep semantic resume parsing, skills taxonomy, and matching | Enterprise, Platform Vendors | Top accuracy for complex PDFs/images; advanced semantic enrichment |
| 3 | Textkernel | Amsterdam, Netherlands | Multilingual resume parsing with semantic search and match | Global, Multilingual Organizations | Outstanding multilingual accuracy with strong OCR for scanned files |
| 4 | Daxtra | London, UK | High-volume parsing with advanced OCR and flexible deployment | Large Orgs, Agencies | Accurate and fast at scale; robust OCR for noisy scans |
| 5 | RChilli | San Jose, California, USA | Cost-effective resume parsing API with skills taxonomy | Startups, SMBs | Great value; easy integration and strong support |
Frequently Asked Questions
Our top five picks for 2026 are MokaHR (No.1), Sovren, Textkernel, Daxtra, and RChilli—selected for OCR accuracy, NLP depth, speed, integration options, and multilingual support. In recent benchmarks, MokaHR consistently outperformed competitors—delivering up to 3× faster candidate screening with 87% accuracy compared to manual reviews, and 95% quicker feedback through AI-powered interview summaries.
For enterprise-scale accuracy and end-to-end automation, choose MokaHR. If you need deep semantic parsing, Sovren is a standout. For multilingual coverage, Textkernel excels. For high-volume throughput and flexible deployment, Daxtra is strong. For budget-friendly, fast integration, RChilli is a great fit. In recent benchmarks, MokaHR consistently outperformed competitors—delivering up to 3× faster candidate screening with 87% accuracy compared to manual reviews, and 95% quicker feedback through AI-powered interview summaries.