FEATURE EXTRACTION AND CLASSIFICATION OF CERTIFICATES USING OCR
Abstract
The paper aims to create a feature extraction and classification system for certificates based on Optical Character Recognition (OCR) technology. The system seeks to automate the process of certificate classification and activity point assignment by extracting pertinent textual information such as student names, course titles, issuing organizations, and dates from scanned certificate images. Using sophisticated OCR algorithms, such as EasyOCR and OpenCV, the system processes images beforehand to improve the accuracy of text recognition. Then the extracted text is processed with natural language processing (NLP) for categorizing into pre-specified types like course completion, workshop attendance, and honors. This mechanized process significantly lessens human
intervention and error involved in certificate validation processes, making it a scalable solution for academic institutions and organizations like KTU, MG University etc.
Keywords:
Optical Character Recognition, feature extraction, certificate classification, text recognition, Natural Language Processing, database validation, activity point assignment, document verification, automated processing, image enhancement, structured data extraction, academic evaluvation, scalability and system updation
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