Similar Document Template Matching Algorithm
Harshitha Yenigalla Bommareddy Revanth Srinivasa Reddy Batta Venkata Rahul Nannapuraju Hemanth Raju
This study outlines a comprehensive methodology for verifying medical
documents, integrating advanced techniques in template extraction, comparison,
and fraud detection. It begins with template extraction using sophisticated
region-of-interest (ROI) methods, incorporating contour analysis and edge
identification. Pre-processing steps ensure template clarity through
morphological operations and adaptive thresholding. The template comparison
algorithm utilizes advanced feature matching with key points and descriptors,
enhancing robustness through histogram-based analysis for accounting
variations. Fraud detection involves the SSIM computation and OCR for textual
information extraction. The SSIM quantifies structural similarity, aiding in
potential match identification. OCR focuses on critical areas like patient
details, provider information, and billing amounts. Extracted information is
compared with a reference dataset, and confidence thresholding ensures reliable
fraud detection. Adaptive parameters enhance system flexibility for dynamic
adjustments to varying document layouts. This methodology provides a robust
approach to medical document verification, addressing complexities in template
extraction, comparison, fraud detection, and adaptability to diverse document
structures.