What Is an ATS and How Does It Parse Resumes?

Understanding Applicant Tracking Systems

An Applicant Tracking System (ATS) is software designed to streamline the recruitment process for employers. These systems have become essential tools for modern hiring, helping companies manage large volumes of applications, organize candidate information, and identify the best talent efficiently.

When you submit your resume online, it likely enters an ATS before a human recruiter ever sees it. Understanding how these systems work—and how they process your resume—gives you a significant advantage in the job market.

What Does an ATS Do?

Applicant Tracking Systems serve multiple functions in the hiring process:

Resume Storage and Organization

ATS platforms create a centralized database of all applicants, allowing recruiters to search, sort, and filter candidates efficiently. Instead of managing hundreds of individual resume files, recruiters can query the database for specific qualifications.

Job Posting Distribution

Many ATS platforms integrate with job boards and career sites, allowing employers to post jobs to multiple platforms simultaneously. This creates a single point of distribution while reaching a broader audience.

Resume Parsing and Extraction

One of the most critical ATS functions is parsing—converting unstructured resume content into structured data that can be searched and analyzed. This includes extracting contact information, work history, education, skills, and other relevant details.

Candidate Screening and Scoring

ATS systems use algorithms to score and rank candidates based on how well their qualifications match the job requirements. This helps recruiters quickly identify the most promising applicants from large pools.

Interview Management

Modern ATS platforms often include tools for scheduling interviews, tracking candidate progress through hiring stages, and collaborating with hiring teams.

Reporting and Analytics

Employers use ATS analytics to measure recruitment metrics like time-to-hire, source effectiveness, and conversion rates across the hiring funnel.

The Resume Parsing Pipeline

Understanding how ATS software parses resumes helps you optimize yours for better results. Here is a detailed look at each stage of the parsing pipeline.

1. File Ingestion and Conversion

When you submit your resume, the ATS first converts your file to plain text. Different file formats are handled differently:

• .docx files: Easiest to parse—the system extracts text directly from the document structure
• PDF files: Requires PDF parsing, which can vary in accuracy depending on how the PDF was created
• Image files: Requires Optical Character Recognition (OCR) technology to extract text

2. Optical Character Recognition (OCR)

For PDF and image files, the ATS uses OCR to convert visual text into machine-readable content. Modern OCR technology is sophisticated but not perfect—handwritten elements, unusual fonts, or low-quality scans can lead to parsing errors.

This is why placing text in images (like skill icons or contact info) is problematic—OCR may not recognize it, and even if it does, the text will not be searchable in the ATS database.

3. Section Detection

After converting to text, the parser analyzes the document structure to identify different sections:

• Contact Information: Name, email, phone, address, LinkedIn URL
• Work Experience: Job titles, companies, dates, descriptions
• Education: Degrees, institutions, graduation dates
• Skills: Technical skills, tools, certifications
• Summary/Objective: Professional summary or career objective

The parser looks for standard section headings to categorize information. Non-standard headings or missing headings can cause the system to misclassify or miss important content.

4. Data Extraction and Tokenization

Once sections are identified, the parser extracts specific data points from each section:

• Contact details: Email addresses, phone numbers are regex-matched
• Dates: Various date formats are normalized (MM/YYYY, Month Year, etc.)
• Keywords: Text is broken into tokens (individual words/phrases) for searching
• Skills: Compared against built-in skill taxonomies

This tokenization process is why keyword optimization matters—your resume must contain the right terms to match against job requirements.

5. Scoring and Output

The parsed data is stored in the ATS database and used for various purposes:

• Searching by recruiters for specific qualifications
• Automated scoring based on job requirements
• Display in the ATS interface for recruiters to review
• Integration with downstream systems (HRIS, assessment tools)

Types of ATS Platforms

ATS software varies widely in complexity and capability. Understanding different types helps you know what to expect.

Enterprise ATS

Large-scale platforms designed for high-volume hiring with advanced features, integrations, and customization options. Examples include Workday, Greenhouse, and Lever.

Small Business ATS

Simpler platforms for companies with smaller hiring volumes, offering essential features at lower cost. Examples include Bullhorn, Zoho Recruit, and Breezy HR.

ATS for Specific Industries

Specialized systems designed for particular industries like healthcare, technology, or staffing. These often include industry-specific features and compliance requirements.

Free/Open Source ATS

Basic ATS solutions for very small companies or startups just beginning to formalize their hiring process.

Common Parsing Failures

Understanding what breaks resume parsing helps you avoid these pitfalls:

Contact Information Issues

• Contact info in headers or footers (often not parsed)
• Contact information in images or graphics
• Multiple columns splitting contact details
• Unusual email addresses that do not match standard patterns

Layout and Formatting Problems

• Two-column layouts that disrupt text flow
• Tables that split content across cells
• Text boxes and sidebars that obscure content
• Headers and footers with important information

Section and Heading Issues

• Non-standard section headings (e.g., "My Work History" instead of "Experience")
• Creative formatting that bypasses standard structure
• Missing section headings entirely
• Inconsistent heading styles throughout

Content Recognition Problems

• Text embedded in images or logos
• Custom fonts not recognized by the parser
• Non-English characters or special symbols
• Acronyms without spelling out first (ATS may not recognize them)

How to Make Your Resume ATS-Friendly

Follow these best practices to ensure your resume parses correctly and ranks well.

File Format Guidelines

• Submit as .docx when possible—most ATS systems parse this format most accurately
• If submitting PDF, ensure it is a clean, text-based PDF (not scanned)
• Follow any specific format instructions in the job posting
• Keep file names professional (FirstName_LastName_Resume.docx)

Formatting Best Practices

• Use a simple, single-column layout
• Stick to standard fonts: Arial, Calibri, Times New Roman, or Garamond
• Use standard font sizes: 10-12pt for body, 14-16pt for headings
• Include clear section headings in plain text
• Avoid tables, text boxes, and graphics
• Do not use headers or footers for important information

Content Organization

• Place contact information at the top of the document body
• Use reverse chronological order for work experience
• Include dates for each position (Month Year or Year)
• Use standard section headings: Contact, Summary, Experience, Education, Skills
• Integrate relevant keywords naturally throughout content
• Quantify achievements where possible

The Human Element

While ATS optimization is crucial, remember that human recruiters ultimately make hiring decisions. Here is how to balance both:

What ATS Cannot Capture

• Context and nuance of your achievements
• Cultural fit and soft skills
• Career progression and growth
• Unique experiences that set you apart

The Best Approach

Optimize your resume for ATS parsing while ensuring it remains compelling for human readers. Use relevant keywords naturally, maintain clear formatting, and include substantive content that showcases your value. A well-optimized resume that also resonates with humans will outperform one optimized for machines alone.

Key Takeaways

• Applicant Tracking Systems (ATS) are software platforms that help employers manage recruitment, store applications, and screen candidates efficiently.
• The resume parsing pipeline includes file ingestion, OCR, section detection, tokenization, and data extraction.
• Different file formats (.docx, PDF) are parsed differently—.docx is generally most accurate.
• Section detection relies on standard headings—use conventional titles like "Experience," "Education," and "Skills."
• Common parsing failures include contact info in headers, two-column layouts, tables, and text in images.
• To make your resume ATS-friendly: use simple formatting, standard fonts, clear headings, and submit as .docx when possible.
• Balance ATS optimization with human readability—human recruiters ultimately make hiring decisions.