Suppose you’ve been asked to hire a candidate for a new position at your company, and you write a stellar job description that really draws in top talent. You get hundreds of applications the first day the job is posted, and more continue to pour in as time goes on. Reading through five, ten, or even twenty resumes is easy. But what do you do when you need to handle resumes in bulk? Is it possible to find the best candidate when you’re working with thousands of resumes?
It turns out to be easier than you’d think— if you have a resume parsing program. A digital resume parser translates the messy stack of resumes on your desk— or the folder of pdfs on your computer— into structured data in a database. Parsed resume data can be used to populate candidate profiles in an applicant tracking system (ATS), and you can screen and sort to your heart's content.
How a Resume Parser Works
A modern resume parser uses artificial intelligence (AI) to transform the unorganized information in a resume into structured data computers can understand, manipulate and act upon. A typical AI resume parser will run a multi-step process. If you want to like to delve deeper into the question ‘what does resume parser do”, here’s a basic summary of what happens behind the scenes.
- The first step is often what we call optical character recognition, or OCR; translating scanned text (i.e., images) into computer-editable text. This starts by simply analyzing the image for light and dark areas: the light areas are categorized as background, and darker areas are classified as characters or shapes that need recognition. Now feature recognition and pattern recognition go to work, processing the images of characters on the page and turning them into digital text. The two techniques are similar but different: in pattern recognition, the software compares the forms on the page to samples of letters and numbers in various fonts and formats. In feature recognition, the software focuses on angled lines, crossed lines, and curves, classifying each character by recognized features. Letters are organized into words and phrases, sentences, and paragraphs.
- . Once the OCR is finished, the fun part begins: data extraction. Now AI modules ‘read’ the text, extracting key information from every part of the resume. Vega, Affinda’s AI engine, uses a holistic approach to not only look at the words on the page but also the structure of the resume to determine exactly what means what. Fields like skills, work experience, job titles, and education are discovered no matter where in the resume they happen to be or what wording is used to describe them. Deep learning algorithms and a cycle of continuous improvement mean the AI engine gets better at reading resumes with every resume processed, and when you’re looking at thousands of resumes processed daily, you’ve got some sniper-sharp extraction capabilities.
- Now the data is condensed into a structured file. Although this may be in many types of formats, think of it as an Excel file where key fields are noted for every single resume. These key fields can be customizable; for instance, if you’d like professional certifications alongside skills work experience and job titles, just add that in. Verification may or may not be a separate step, and you can get confidence levels for every field or every resume.
- Extracted data can be imported into your applicant tracking system, ATS. If your resume parsing software has an API that allows direct integration into your ATS or recruiting software, this step may automatically happen once the resume data has been extracted.
Why You Need a Resume Parser
A CV resume parser isn’t only important because it makes your workload manageable when you’re handling a popular job with lots of applicants. It’s a great idea in the long run because it streamlines your workflow, provides you with great records, and automatically generates a candidate pool from all the jobs you’ve ever posted. It also gives you a chance to get a bird's eye view of all the hiring and applying going on through your office— potentially an invaluable source of information when you need to locate a qualified candidate quickly.
Resume parsers can also be the first step to bias-free hiring. When you’ve got your resume data parsed, it’s easy to generate a clean copy of each resume with all potentially bias-inducing info redacted. Fields like name and location are entirely irrelevant to the decision-making process and they can induce unconscious bias very quickly.
A resume parser can also be used to redact contact information when sharing a resume with clients, ensuring everything stays in the right channels.
The Best Resume Parser
Now that you know what a resume parser does and why you need one, what is the best resume parser out there? Here’s a short list of features any good resume parser should have:
- AI-based parsing that learns from its own mistakes, rather than a template-based approach that keeps doing the same thing wrong over and over
- A robust verification module that gives you a range of confidence for any results
- Quick, reliable parsing when you need it
- A free trial that allows you to test the software thoroughly.
This last one is a big one. Reading through reviews and researching software on the web is great as far as it goes, but you won’t know how any resume parsing technology will do on your real-life use case until you try it out. Great software will have a team that stands behind it and which is more than happy to have you take it out for a test drive before you make a commitment of any kind.
We may be biased, but as far as we can tell there’s no resume-parsing software better than Affinda. Built on all the newest advances in AI technology, this resume parser never steps out of its continuous improvement cycle— and it’ll adapt to your specific industry or use case till it knows all the lingo like a pro. You can try out the resume parser using a free online tool, or write the team for a free API key and run a full-scale trial.
