Advanced Resume Parser:
Accurate Data Extraction with NLP

Efficiently Managing Diverse Resume Formats for Better Job Matching

TECHNOLOGY USES

Python

Python

Flask

Flask

SpaCy

SpaCy

Scikit-Learn

Scikit-Learn

PyTorch

PyTorch

TensorFlow

TensorFlow

UNIQUE SELLING PROPOSITION

The resume parser is a unique system designed and managed with a high-end backend for managing essential data input. It utilizes natural language processing (NLP) to parse data from all sections of a resume and feed it to the relevant segment for management. Additionally, it features an AI and NLP-based job match score system that matches job descriptions with generated resumes to produce the best match score.
Problem Icon

PROBLEM STATEMENT

Resumes come in all shapes and sizes, from sleek, single-page PDFs to text-heavy Word documents crammed with information. This variety, while great for showcasing individuality, can be a nightmare for traditional parsing tools designed for clean, structured data. Imagine a program trained on neatly formatted spreadsheets suddenly having to navigate a document that looks more like a wild jungle of text, with skills scattered throughout and headings used inconsistently. Extracting the right information from this chaotic landscape becomes a major hurdle.
Solution Icon

Solution STATEMENT

To tackle the jungle of resume formats, we use a multi-step process that converts each document into a single, clean text file. This involves removing special characters, extra spaces, and unnecessary formatting, like italics or bolding. We also handle line breaks and headers to ensure a consistent structure. Think of it as taking a machete to the textual jungle, clearing a path for the model to navigate and find the valuable information hidden within.
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