Document Classification and ROI Extraction for Noisy Scanned Forms
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Solution Overview
Problem
Conventional template-based matching systems face challenges in accurately extracting text information from scanned documents due to variations in document types, templates, and background noise, leading to inefficiencies and reduced accuracy in text extraction.
Innovation Solution
A method and system utilizing a trainable Deep Learning based multi-layered Neural Network for document classification, combined with multistage pre-processing techniques including unsupervised clustering, color masking, contour approximation, and dynamic thresholding to effectively remove background noise and enhance text detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional template-based matching is used for text extraction, then the system requires prior knowledge of document templates and classification, but the system cannot handle variations in document types, templates, and scanning conditions
Solution Approach 1:
The system performs self-service by automatically detecting document type and template characteristics without requiring external classification or pre-loaded template knowledge bases. The algorithm autonomously adapts to different document formats and scanning conditions, eliminating the need for manual template registration and document categorization steps.
Solution Approach 2:
The extraction system achieves universality by designing a single unified algorithm that can process multiple document types (ID cards, certificates, forms, reports) and various templates simultaneously. The system maintains one core extraction engine that adapts to different document structures rather than requiring separate specialized modules for each document type.
2Measurement precision
If background noise removal techniques are applied to improve text extraction accuracy, then text extraction accuracy improves, but the preprocessing complexity and time consumption increase
Solution Approach 1:
The system performs preliminary action by executing targeted preprocessing operations (denoising, contrast enhancement, binarization) before text extraction. These preprocessing steps are applied selectively based on detected document characteristics and noise levels, preparing the image in advance for more accurate and efficient text recognition without performing unnecessary processing on already clean images.
Solution Approach 2:
The preprocessing approach is made dynamic by adjusting processing intensity and selecting specific preprocessing operations based on real-time analysis of image quality metrics. The system adaptively determines the appropriate level of denoising and enhancement required for each document, avoiding over-processing of high-quality images while applying stronger preprocessing to degraded scans, thus optimizing the balance between accuracy improvement and processing time.
3Adaptability or versatility
If a generic background removal approach is developed to address document type and template variations, then the system can handle diverse documents, but the accuracy of background removal decreases due to equal variation in templates
Solution Approach 1:
The system applies local quality by performing template-specific optimization within the generic framework. After detecting document type and template characteristics, the algorithm adjusts preprocessing parameters, noise filtering strength, and text region identification thresholds locally for each template category. This allows the generic system to achieve near-specialized performance by tailoring processing parameters to local template characteristics while maintaining overall versatility.
4Productivity
If ROI detection techniques are applied to focus on text information, then text extraction efficiency improves, but the difficulty of detecting and measuring text regions increases due to noise and varied layouts
Solution Approach 1:
The system applies segmentation by dividing the document processing into distinct stages: document type classification, template identification, text region detection, and text extraction. Each stage processes a specific aspect of the document, breaking down the complex task of ROI detection into manageable sub-tasks. This segmented approach simplifies each individual step while maintaining overall efficiency, as each segment can be optimized independently based on its specific requirements.
Data Source
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AI summary
Variation in received documents types and templates used for each document type poses challenge in developing a generic background noise removal approach for automatic text information extraction technique. Embodiments herein provide a method and a system for document classification and text information extraction. Time efficient and accurate text detection engine-based Region of Interest (ROI) technique is provided to accurately identify text region followed by a multi-layered neural network based architecture for enhanced classification accuracy to identify the type of document. A multistage image pre-processing approach is provided for efficient, effective, and accurate background noise removal from the classified document, which includes unsupervised clustering, identification, segmentation, masking, contour approximation, selective subtraction, and dynamic thresholding. Further, text information extraction is applied on the pre-processed image to determine co-relation between text information blocks and associate values with the labels for attributes to be extracted to extract final text.