Resume parsing is the process of automatically extracting information from a resume and converting it into a structured format that can be easily processed and analyzed by computers. This technology has become increasingly important in the recruitment industry as a way to save time and improve the accuracy of candidate selection. Resume parsing primarily employs natural language processing (NLP) techniques to extract specific data points from resumes, such as the candidate's name, contact information, education, work experience, skills, and more. The software analyzes the text of the resume, identifying keywords and phrases that match specific data fields. Once the information is extracted, it is converted into a structured format that can be easily imported into an applicant tracking system (ATS) or recruitment management system, where recruiters can search and filter resumes based on specific criteria. There are several benefits to using resume parsing software, including saving time by automating the process of data entry and reducing errors, improving the accuracy of candidate selection, and reducing bias in the recruitment process. Some popular resume parsing software includes Nanonets OCR, Sovren, Rchilli, Textkernel, Talismatic, DaXtra, and Hiretual, each offering unique features and capabilities such as handling multiple file formats, supporting multiple languages, and providing semantic matching capabilities.