Grayscale Image Text Recognition via Layered Erosion
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Solution Overview
Problem
Existing image text recognition methods are heavily influenced by subjective factors and lack generality, resulting in poor text recognition accuracy when scene conditions change, as they rely on manually set features and rules that fail to adapt to new environments.
Innovation Solution
The method involves converting images into grayscale, segmenting them into layers based on pixel grayscale values, performing image erosion on each layer to create feature layers, overlaying these layers, dilating connected regions to identify text regions, and performing text recognition on these regions to improve accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual feature setting and rule-based methods are used for image text recognition, then the method works well for currently designed scenes, but text recognition accuracy deteriorates when scene conditions change
Solution Approach 1:
The patent segments the grayscale image into multiple layers based on grayscale value ranges, performing erosion operations on each layer separately. This segmentation approach allows the system to handle different text characteristics in different grayscale ranges, improving both accuracy and adaptability to various scene conditions without requiring manual feature reconfiguration.
Solution Approach 2:
The patent dynamically adjusts erosion parameters for different grayscale layers, allowing the erosion strength and other parameters to vary based on the grayscale value range of each layer. This parameter adaptation enables the system to maintain high text recognition accuracy across different scene conditions without manual intervention.
2Productivity
If image erosion is performed on the entire grayscale image as a whole, then the processing is simple, but connected regions may be missed or falsely recognized
Solution Approach 1:
The patent divides the grayscale image into multiple layers based on grayscale value ranges, and performs erosion operations on each layer independently. This segmentation enables precise identification of connected regions by capturing subtle variations in grayscale values, thereby improving measurement precision without significantly increasing processing complexity.
Solution Approach 2:
The patent performs erosion operations on multiple grayscale layers, which is more than a single-layer approach. By applying erosion to each layer and then synthesizing the results, the system achieves higher precision in connected region recognition while maintaining reasonable processing efficiency through parallel operation.
3Measurement precision
If grayscale layers are segmented and erosion is performed on each layer, then erosion effect on each layer is improved and recognition accuracy increases, but the device complexity increases
Solution Approach 1:
The patent segments the grayscale image into multiple layers based on grayscale value ranges, performing erosion operations on each layer separately. This segmentation improves erosion effectiveness and text recognition accuracy by treating different grayscale regions independently, while the modular nature of the approach keeps processing complexity manageable.
Solution Approach 2:
The patent uses a universal erosion operation that can be applied to multiple grayscale layers with the same or different parameters. This multi-functional approach allows the system to handle various text characteristics across different layers using a unified framework, reducing the need for separate complex processing pipelines for each layer.
Data Source
AI summary
An image text recognition method includes converting an image into a grayscale image, and segmenting, according to layer intervals to which grayscale values of pixels in the grayscale image belong, the grayscale image into grayscale layers with one corresponding to a layer interval, performing image erosion on a grayscale layer to obtain a feature layer corresponding to the grayscale layer, the feature layer including at least one connected region; overlaying feature layers to obtain an overlaid feature layer, the overlaid feature layer including connected regions; dilating connected regions on the overlaid feature layer according to a preset direction to obtain text regions; and performing text recognition on the text regions on the overlaid feature layer to obtain a recognized text corresponding to the image.