Border Detection Model for Document Images
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Solution Overview
Problem
Existing border detection methods are inaccurate due to interference from dark and light backgrounds in images, leading to incorrect detection of document or certificate regions.
Innovation Solution
A border detection method that involves detecting first straight line segments in an image, generating candidate borders, extracting border features, and using a border detection model to determine the similarity between candidate borders and actual borders, thereby improving detection accuracy without relying on selection rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional border detection methods are used, then the detection process is simple, but the detection accuracy is low due to background interference
Solution Approach 1:
The detection process is segmented into multiple stages: initial border candidate detection, filtering to obtain second candidate borders, feature extraction, and final classification using the border detection model. This segmentation allows each stage to focus on specific aspects, improving overall accuracy while managing complexity through modular processing
Solution Approach 2:
The border detection model serves as an intermediary between the extracted border features and the final detection result. This model, trained on border feature data, mediates the classification process to determine whether candidate borders are actual borders, thereby improving accuracy without requiring complex rule-based systems
2Measurement precision
If selection rules are used to determine actual borders, then the process is straightforward, but the accuracy is insufficient due to background interference
Solution Approach 1:
Traditional mechanical selection rules are replaced with a machine learning-based border detection model. This model learns from training data to automatically classify border candidates, substituting simple but inaccurate rule-based mechanics with a more sophisticated statistical decision system that handles background interference effectively
Solution Approach 2:
The system extracts multiple border features (such as line segment characteristics, geometric properties, and contextual information) and uses these parameter changes to feed into the detection model. By transforming the detection problem into a multi-parameter classification task, the system achieves higher accuracy in distinguishing actual borders from background elements
Data Source
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AI summary
Disclosed in the present application are a border detection method, server, and storage medium, relating to the technical field of image processing. The method comprises: a first linear section of an image to be detected, the image to be detected comprising a target area; on the basis of the first linear section, generating first candidate borders of the target area; acquiring second candidate borders of the target area from the first candidate borders; extracting the border features of the second candidate borders; and, on the basis of the border features of the second candidate borders and a pre-established border detection model, acquiring the actual border of the target area from the second candidate borders. By means of performing detection on the image to be detected, generating first candidate borders and filtering second candidate borders from the first candidate borders, the present application acquires the actual border of the target area on the basis of the border features of the second candidate borders and a border detection model. The present method does not rely on selection rules, but instead performs detection on the basis of border features and a border detection model, and therefore the detection results are more accurate.