Document Prediction and Denoising for OCR Accuracy
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Solution Overview
Problem
Current Optical Character Recognition (OCR) systems face challenges in accurately extracting personal data from scanned documents, particularly due to poor image quality and noise, which complicates compliance with data protection regulations like GDPR, as they require significant effort and time for preprocessing and may produce incorrect data.
Innovation Solution
A method and system that predicts the type of document using keypoint matching, crops the document to extract relevant fields, denoises the fields, and performs OCR to detect characters, incorporating a document prediction module, cropping module, denoising module, and OCR module for accurate personal data extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR is applied to scanned documents with poor quality and noise, then the process requires significant preprocessing effort and time, but the extraction accuracy remains low and errors occur
Solution Approach 1:
The system performs preliminary document type prediction using keypoint matching before OCR processing. This allows the system to automatically identify document types and apply appropriate preprocessing techniques in advance, reducing the time and effort required during actual OCR operations while maintaining high accuracy
Solution Approach 2:
The system dynamically adjusts preprocessing parameters based on detected document types and image quality metrics. By changing parameters such as noise threshold values, cropping regions, and processing intensity according to the specific document characteristics, the system achieves high extraction accuracy without excessive preprocessing time
2Loss of information
If additional marks and writings are present on scanned images for authentication or categorization, then the documents contain valuable information, but the OCR process becomes more challenging and error-prone
Solution Approach 1:
The system segments the document image into different regions based on document type prediction and keypoint detection. By dividing the document into distinct areas (e.g., text regions, marks, signatures), the system can process each region with appropriate techniques, maintaining both information completeness and recognition accuracy
Solution Approach 2:
The system applies different processing quality levels to different parts of the document based on their importance and characteristics. Critical text fields receive enhanced processing while less critical areas with marks receive appropriate but reduced processing, maintaining accuracy where needed while managing overall computational resources
3Adaptability or versatility
If different languages with various characters and alphabets are processed, then the system can handle diverse documents, but the complexity of character recognition increases
Solution Approach 1:
The system uses a universal keypoint matching approach that works across multiple document types and languages. By detecting common geometric features and patterns that are consistent across different languages and document formats, the system achieves multi-language capability without proportionally increasing complexity
Solution Approach 2:
The system creates a standardized representation of document structures through keypoint matching that can be applied across different languages. This abstract representation allows the same processing pipeline to handle various languages by mapping their specific characters to the universal structure, reducing the need for language-specific complex processing
Data Source
AI summary
Methods and systems for extracting personal data from a sensitive document are provided. The system includes a document prediction module, a cropping module, a denoising module, and an optical character recognition (OCR) module. The document prediction module predicts type of document of the sensitive document using a keypoint matching-based approach and the cropping module extracts document shape and extracts one or more fields comprising text or pictures from the sensitive document. The denoising module prepares the one or more fields for optical character recognition, and the OCR module performs optical character recognition on the denoised one or more fields to detect characters in the one or more fields.