Are you wondering what is a resume parser and what does it do? Here in this post, I am going to explain everything about this.
Resume parsing, also known as CV parsing, helps automate the resume screening and sorting process. It also helps to build structured and data-driven hiring models.
Description of Resume Parser
Resume parsing is the process of automatically extracting information from a resume so that HR teams can sort the information. After the information of the candidate is stored in the database so that HR teams can then analyze with keywords.
If you add semantic search with parsing services, it helps HR teams to match jobs to the ideal candidates. Thus it becomes quite easy to find the best candidate.
AI and Machine Learning in Resume Parsing
Traditional parsing was based on rule-based algorithms, but with the advent of AI and machine learning, parsing has become faster and more accurate.
We can better predict and do a fast statistical analysis of the resume with natural language processing, optical character recognition (OCR), and machine learning.
See how you can choose the best HR Software?

Benefits of Resume Parsing Software
HR teams can easily manage resume data with parsing and storage.
Convert unstructured data to a structured form.
Multi-languages parsing support.
Transfer of data to online applications without human intervention.
You can easily search and sort candidates in your database.
Semantic matching features match the most compatible candidate.
The resume parsing API technology is extremely cost-effective and saves time.
Get information about the candidates’ contact information, skills, work history, educational details easily.
CV extraction technology helps to find candidates quickly when recruiters have to scan through hundreds of candidates.
Tips That Will Help Candidates to Pass-Through Parsing Software
Every candidate has a style of writing a resume, and this makes parsing a challenging task. While creating a resume, they can keep certain points in mind to increase the chances of making it to further rounds. Here are the tips.
Ensure you don’t use an image format resume.
Avoid unnecessary usage of tables and columns.
You can use the following format or file system to make a resume for easy parsing like MS Word, Google Doc, PDF, ODT, HTML, TXT, etc.
Use standard fonts on the document.
Try to summarize your resume details so that you can say everything in minimum pages such as 2 or 3.
Avoid using text boxes and repeated information.
At RChilli we are using the world-class TensorFlow library for machine learning to build our models that quickly adjust to new formats. We are helping many clients across with our best-in-class resume parsing services.
If you still have any queries, we will be glad to help. Feel free to talk to us.