As a result of the development of the internet hiring platform Candidates can post their resumes with ease on the job application website. This can cause an enormous volume of resumes to be sent in. Because of this, hiring new staff and going through a lot of resumes are difficult tasks for the human resources department. Additionally, the styles of resumes uploaded by candidates vary in terms of writing style, typefaces, font sizes, colours, and other elements. Choosing the best applicant for a job requires human resource departments to sift through all of the resumes that applicants post. Therefore, for this project, I suggest developing a resume parser that uses NLP to help the recruiter or human resources department extract the resume’s detailed information that is required to move on with the applicant’s process and minimize job errors. Three processes make up this suggested resume processing system: 1) Get the candidate’s resume files 2) Convert the resume file to text. 3) Gathering the information that is required. Only pertinent information required for resume selection will be extracted by the system: name, including names, university, skill, email, and phone number. Furthermore, the system has the ability to provide the both job category and recommended job to facilitate the recruiting selection process for recruiters.