In today's job market, the number of applications is overloaded. HR and recruiters are looking to simplify their process of reviewing resumes and extracting details from cover letters. In this blog, I am going to discuss techniques, tools and resources for parsing personal details from resumes faster and more efficiently helping to simplify the process

What is Resume Parsing

Resume parsing is the process of converting unstructured resume data into structured format. This means converting any resume documents into useful information like personal details of the candidate like name, email, address, phone number, projects, work experience, education, certifications etc. without manually looking into every resume.

What are Different Resume File Formats

Candidates use many popular resume formats to submit along with the job application and a good resume parsing tool should support any common file format. Below are some of the formats.

• PDF (Portable Document Format) 

• Microsoft Word Documents (DOC and DOCX):

• Text Files (Txt) 

• HTML (HyperText Markup Language):

• RTF (Rich Text Format)

What are the Benefits of Resume Parsing

• Efficiency: Saves lots of time by automating the data extraction process.

• Accuracy : Reduce Human Mistakes in Data Entry

• Scalable: Handle Large Volume of resumes.

What are the Techniques for Resume Parsing

• Keyword-Based Parsing: Search for specific words, phrases and patterns within the resumes.

• AI-Based Parsing: Uses Machine Learning to get information from the resumes.

How Parser.Expert  helps with Resume Parsing

Parser.Expert is a tool which uses advanced AI capabilities to parse resumes of any file format. Here are some of the added benefits of using the tool

• Customized Parsing: Customize the parsing process to adapt to your recruitment needs. 

• Integration Capabilities: You can easily integrate your current HR Systems with Parser.Expert for easier and more efficient workflow

• Scalable: capable of handling thousands of resume

checkout the video tutorial below