Can AI Really Parse Resume PDF Well Enough?

Exploring the evolution of AI and OCR in resume parsing, from early limitations to advanced AI parsers offering accuracy and efficiency.
December 20, 2023
4 min
Using AI to Parse PDF Resumes Improves Parsing Accuracy
Using AI to Parse PDF Resumes Improves Parsing Accuracy

In the early stages of OCR development, basic parsers relied on predefined resume templates and formats. These can be are incredibly limiting. This made job application difficult to automate, especially when there were different file formats in play.

With this in mind, it’s easy to get concerned about whether AI can truly parse a PDF resume with high enough accuracy. However, there has been significant progress since the early days of OCRs. With the advent of AI resume parsers, companies around the world are experiencing unrivaled parsing performance. Extracting data from a PDF file or Microsoft Word documents is no longer a problem.

Here’s why:

What is an AI-based Resume Parser?

An AI-based resume parser is a piece of software that extracts information from CVs. It then organises the data into a format that computers can understand.

How does a resume parser work? Instead of having to manually read through resumes you can use an AI resume parser. It can do all that for you and enter data into your applicant tracking system.

All you have to do is decide which resume fields are important to your recruiting process. Your parser will import it into your ATS for further analysis.

While a reputable AI resume parser will be able to extract 100s of fields from a resume file. Some common ones that HR departments and recruiters find value in include:

●    Applicant skill sets

●    Previous job titles and work experience

●    Personal contact details

●    Educational achievements

●    Length spent in each job role

It takes a fraction of the time for an AI to complete this process than it would take a human to do it.  Humans take more time to read and input this important data into an application system. Not to mention, an AI-based resume parser frequently performs better than a human when measuring output accuracy. That is something that’s particularly important for subsequent recruiting processes.

AI isn’t perfect and flawless. It still makes mistakes, especially if the solution has never parsed a unique resume format. However, the speed at which AI can learn and rectify its mistakes in future parses more than makes up for any initial mistakes.

Simply put, the more resumes your AI parses the better it becomes at what it does, just like a human – only better.

Why the Type of OCR  Matters for PDF Resume Parsing

Optical Character Recognition technology plays a pivotal role in parsing resumes. While it isn’t a new technology in and of itself, subsequent iterations have improved on its initial performance.

Without this technology, it wouldn't be possible to parse data from printed or handwritten text from images or scanned resumes.  Especially if it is necessary to export that data into formats that machines can understand and edit.

Affinda resume parser recognizes fields in a PDF resume

In its early iterations, OCR relied upon basic pattern recognition methods. Those where enough to decipher and interpret its content extraction and that is fine for simple document-based parses.

However, things become a bit more challenging with PDFs. OCRs perceive them as images rather than traditional text. That’s why not all types of OCR solutions can process PDFs as successfully.

These are a few of the common marketplace variants:

1. Simple Optical Character Recognition Software

A basic OCR engine operates on a template-based extraction process. This requires from you to create a different rule set for each type of resume you want to parse.

It then uses pattern-matching algorithms to dissect text images and match them character by character against its templates. However, this methodology comes with significant constraints. Namely, the more rule sets you provide, the lower the accuracy and speed of the solution becomes.

Not to mention that it would take countless hours to manually create these rule sets. Consequently, it’s not much better than manually reading a resume.

2. Intelligent Character Recognition Software

ICR software is the next iteration of OCR which mimics human reading patterns. This allows it to parse handwritten and more complex documents.

Typically, these OCRs employ advanced technologies to analyse text and images at a much deeper level than basic OCRs. These technologies include machine learning - more specifically neural network.  

It looks at various features of text such as curves, lines, and intersections in order to accurately parse resumes.

3. Intelligent Word Recognition

IWR is an even more advanced iteration of ICR software. It speeds up parses by analysing entire words instead of focusing on a single character at a time.

In the grand scheme of things, this serves to further improve the efficiency of your resume parsing.

However, OCR technologies on their own are not enough to parse resumes well enough.

How AI Dramatically Improved OCR Resume Parsers

OCRs are just one aspect of parsing a resume at a professional level. You also need AI and intelligent document processing.

These technologies allow for high-level automation and accuracy. These offer significant benefits over manual and even basic OCR processing. They allow you to extract but also classify, categorise, and validate the data.

They do this through AI and deep learning algorithms such as:

●    Convolutional Neural Networks (CNNs): These networks have the capacity to understand and extract visual features through convolutional and pooling layers. Through localised pattern recognition and spatial correlations, CNNs enable precise detection and character recognition.

●    Recurrent Neural Networks (RNNs): RNNs, on the other hand, specialise in handling sequential data such as ordered text and paragraphs used in resumes. The power of these neural networks lies in their ability to use internal memory or "hidden state."

This enables them to encapsulate context and relations between consecutive resume elements. These features help it navigate variable-length inputs to decipher handwriting and other text elements.

Both CNNs and RNNs are trained on significant volumes of labelled data. This allows OCRs to reach remarkable levels of accuracy in both text recognition and extraction. All this - regardless of the intricacy of fonts, diversity of layouts, and image resolution.

All this translates into AI being able to parse resumes at a performance level far beyond humans.

Some of these parsing benefits include:

●    Improved Accuracy and Efficiency: AI-based OCR parsers consistently deliver heightened accuracy in data extraction and interpretation. This in turn expedites processes and improves efficiency.

●    Enhanced Applicant Management: AI enables a new level of sophistication to document management, allowing for streamlined indexing, categorisation, and retrieval of information. This makes it easier and faster to find suitable candidates in your applicant tracking system.

●    Faster Decision Making. The swift and dependable nature of AI-enhanced OCR empowers you to make faster decisions. With its help, you can spend less time on manual processes.

●    Cost and Resource Savings. AI systems significantly reduce the need for manual intervention. This means you save a significant amount of time and money on your recruiting process. This allows you to optimise your resources and put them towards something more meaningful.

●    Improved Applicant Experience. AI-powered OCRs can parse diverse resume formats. This saves your applicants from having to manually input their information.

Such a simpler, easier applicant process which results in more submissions. Consequently, it creates a larger database of eligible candidates.

Overall, the union of AI and OCR isn't just a technological evolution. It's a paradigm shift that enables a new era of accuracy, efficiency, and agility when it comes to professional resume parsing.

Start Parsing Your PDF Resumes with Improved Accuracy and Efficiency Today

At Affinda, we integrate industry-leading AI innovations into our resume parser. Yes, it offers world-class performance when it comes to PDF formats as well.

By integrating deep learning and neural networks your candidates can enjoy a smoother application process. As a result, you can save countless hours on manual reading processes. With 100s of resume fields available for extraction, you can tailor our solution to fit your needs.

Our resume reader is also capable of extracting data from resumes in 56 languages. This enables you to expand your recruiting efforts to a global scale.

Experience our enterprise-grade resume parser for yourself with a free trial. Experience just how well our AI can parse a PDF resume and more.

Share this post

The world’s most accurate resume and job description parser.

Try the most accurate resume parser on the market. Using the latest AI technology, you can extract over 100 fields per resume with unmatched accuracy.

Affinda's Resume Parser wins against competition in blind tests over and over again.

Transform piles of job descriptions into organized data you can actually search and use to find the best candidates.

Job Description Parser uses the same technology as the Resume Parser, which means the accuracy and speed are unmatched.

Make the most of the rich data extracted from resumes and jobs:

- Find the best candidates
- Find the best jobs for candidates
- Score candidates based on compatibility
- Discover similar database candidates

Take the bias out of resumes and promote fair candidate selection to make your recruitment process best in class by using Affinda’s Resume Redactor.