Research on the AI Evaluation and Matching System for Automatic Text Recognition, Feature Information Extraction and Resume Based on Artificial Intelligence

This project plans to use a method that combines natural language processing (NLP) and optical character recognition (OCR) to realize automatic identification and key information extraction of resumes. First, a multi-level classification method is proposed based on the characteristics of personal resumes, which includes feature extraction and weight calculation, thereby achieving accurate identification and classification of resume information. We need to use a matching algorithm to semantically compare job requirements and resume content to obtain a suitability score. The natural language processing method proposed in this project is based on deep learning and uses a two-way long-term memory network and attention mechanism to achieve accurate understanding of text content. Then an OCR system based on convolutional neural network (CNN) is proposed to improve the recognition accuracy of non-standard text. The simulation experiment showed that the text recognition rate of 2,000 resumes was 98%, the accuracy rate of text recognition was 98%, the accuracy rate of job matching was 95%, and the accuracy rate of job matching was 93%. This method significantly improved the efficiency of traditional manual screening.