Resume parsing, also known as CV parsing, is a technological process that extracts and organizes data from resumes into a structured format. The goal of resume parsing is often to simplify and automate the process of reviewing resumes, which can save significant time and resources, especially for HR departments and recruiters.

This process involves utilizing natural language processing (NLP) and machine learning algorithms to identify crucial information such as candidate's name, contact details, work experience, educational background, skills, and more. The extracted data is then categorized and stored in a systematic manner, allowing for efficient searching, analysis, and management.

In an increasingly digital world, resume parsing is becoming a go-to tool for many platforms. It not only reduces the manual effort required of end-users to input their data but also enhances the accuracy and speed of candidate selection, making the recruitment process more efficient and effective.

At Sensible, our AI-powered platform provides robust resume parsing capabilities that are customizable, accurate, and fast.

Resume parsing, also known as CV parsing or resume extraction, is the process of converting unstructured resume data into a structured format. Here's how it generally works:

‍Document Input: A resume in various formats - such as .doc, .pdf, .txt, or even scanned images - is uploaded into the resume parser.

‍Preprocessing: The parser software performs preprocessing steps to prepare the document for extraction. This can include Optical Character Recognition (OCR) for scanned images or PDFs, removing special characters, and converting the entire text to a standard format.

‍Parsing: The parser then analyses the standardized text to identify and tag different sections such as contact information, work experience, education, skills, and so on. Advanced parsers, like Sensible, can even handle complex structures such as tables, checkboxes, and handwriting.

‍Data Extraction: After tagging, the parser extracts relevant data points based on predefined categories or tags. The parser converts these data points into a structured format, making it easy to store, manage, and analyze.

‍Output: The parser generates an output usually in a structured format like JSON or XML that can easily be incorporated into various applications and systems.

It's important to note that the quality and accuracy of resume parsing can significantly vary based on the parsing software used. Sensible’s advanced AI-driven approach to resume parsing ensures high accuracy and versatility, capable of handling various document types and layouts, even ones it has not encountered before. This provides users with reliable and efficient extraction of data from resumes.

Building a resume parser involves multiple steps, often requiring specialized technical knowledge. Here is a high-level overview of the process:

‍Document Upload: Implement a feature that allows users to upload their resumes in various formats such as DOCX, PDF, or TXT.

‍Text Extraction: Use a text extraction tool or library to read and extract text from the uploaded resumes. This process may involve Optical Character Recognition (OCR) for scanned documents or images.‍

Text Cleaning: Once the text is extracted, clean it by removing unnecessary spaces, symbols, and formatting to ensure better parsing.‍

Keyword Identification: Identify relevant keywords that correspond to the data you want to extract from resumes, such as names, contact details, skills, job titles, education details, and more.‍

Natural Language Processing (NLP): Apply NLP techniques to understand the context around these keywords. This helps to accurately extract and categorize data.‍

Information Extraction: Based on the identified keywords and their context, extract the necessary information and structure it in a useful format like JSON or CSV.‍

Validation and Testing: Validate the extracted information to ensure its accuracy. This involves testing the parser with various types of resumes and improving it as necessary.

While these steps provide a basic outline, building a resume parser from scratch can be complex and time-consuming. That's where Sensible comes in.

Sensible offers an efficient and reliable solution for parsing resumes. It's powered by AI, uses natural language processing, and provides a simple to integrate API. All you need to do is upload your documents and describe the data you need, and Sensible will extract it for you instantly.

Parsing a resume for skills can be accomplished efficiently and accurately with a tool like Sensible. Here's how the process works:

Sensible reads and understands the content of the resume, much like a human would. It uses LLMs (Large Language Models) like GPT-4 to recognize and extract relevant pieces of information, such as the skills a candidate has listed.

To parse a resume for skills, you simply need to upload the resume to the Sensible platform and select the resume configuration. Sensible will then automatically identify and extract the skills listed in the document, regardless of the format or structure of the resume.

By using natural language processing, Sensible can understand a wide variety of ways that skills might be listed on a resume, whether it's in a dedicated skills section, mentioned within job descriptions, or listed in other parts of the document.

Once extracted, the skills data can be used for various purposes like matching candidates to job requirements, ranking candidates based on specific skill sets, or analyzing skills trends across your candidate pool.

By automating the process with Sensible, you can parse resumes for skills quickly and accurately, saving significant time and reducing the risk of manual errors.

Sensible's advanced resume parser is designed to handle a broad spectrum of resumes or CVs, regardless of their format or structure. Sensible's resume parser uses AI to understand and extract information from resumes with diverse layouts, structures, and designs. Whether the resume is chronological, functional, or a combination of both, our parser can identify and capture the critical information it contains.

Sensible's resume parser is also equipped to manage resumes from different industries and job functions, thanks to its ability to identify a vast array of professional jargon, acronyms, and terminologies.

Furthermore, it can even process resumes in multiple languages, making it a versatile tool for global companies and multilingual HR tech platforms.

In essence, whether the resumes are structured or unstructured, text-heavy or design-centric, in English or another language, Sensible's resume parser can accurately parse them.