Advanced Resume Parser:
Accurate Data Extraction with NLP
Efficiently Managing Diverse Resume Formats for Better Job Matching
TECHNOLOGY USES
Python
Flask
SpaCy
Scikit-Learn
PyTorch
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 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 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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