Form Recognition via Structural Line Detection and Noise Filtering
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Solution Overview
Problem
Current OCR technologies often fail to accurately recognize forms in image files, producing garbage characters and unable to correctly identify forms, especially in images of poor quality.
Innovation Solution
A form recognition process involving straight line detection, feature extraction, and similarity calculation using a server that conducts connected component analysis, line width removal, character line segment filtering, and feature vector generation to identify forms by matching them against template forms based on structural features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing OCR technology is used to recognize forms in image files, then the processing speed is maintained at acceptable levels, but the recognition accuracy deteriorates significantly, producing garbage characters and failing to identify forms correctly
Solution Approach 1:
The patent segments the form recognition task into distinct modules: straight line detection module, feature extraction module, and form identification module. This segmentation allows each module to specialize in specific aspects, improving overall accuracy while maintaining processing efficiency. The segmentation separates form structure analysis from character recognition, enabling accurate form identification even when character recognition fails.
Solution Approach 2:
The patent extracts and removes characters and text content from the form image processing pipeline. By taking out character recognition from the form identification process, the system can accurately identify form structures without being distracted by text elements. The feature extraction module specifically extracts form boundaries and structural features while excluding character information, solving the contradiction between processing speed and recognition accuracy.
2Measurement precision
If traditional OCR processes all image content uniformly, then the processing simplicity is maintained, but the noise and characters interfere with form recognition accuracy
Solution Approach 1:
The patent extracts and removes harmful elements (characters and noise) from the image processing pipeline before form recognition. The straight line detection module specifically targets form boundaries and ignores character strokes, effectively separating useful form information from harmful character interference. This extraction approach maintains processing simplicity while eliminating noise interference.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. The straight line detection module focuses specifically on detecting horizontal and vertical lines that form form boundaries, while character regions are either ignored or processed separately. This local quality differentiation allows precise form boundary detection without being affected by character noise throughout the entire image.
3Measurement precision
If form recognition includes comprehensive feature analysis, then the recognition accuracy is improved, but the processing time increases
Solution Approach 1:
The patent segments the feature analysis into essential and non-essential components. The feature extraction module identifies and extracts only the critical form boundaries and structural features necessary for accurate form identification, while omitting redundant character-level analysis. This segmentation of feature analysis maintains high recognition accuracy while minimizing processing time by focusing computation only on essential form structures.
Data Source
AI summary
Embodiments of the present application relate to a form recognition method, a form recognition system, and a computer program product for recognizing forms. A form recognition method is provided. The method includes conducting a straight line detection of a form in a form binary image to acquire a plurality of form boundaries of the form and a plurality of positional relationships between the plurality of form boundaries, extracting a plurality of features from the form using the plurality of form boundaries and the positional relationships between the plurality of form boundaries, establishing a feature vector associated with the form based at least in part on the plurality of features, calculating similarities between the form and respective ones of a plurality of template forms based at least in part on the feature vector of the form, and identifying the form based on the calculated similarities.