Text Localization via Multi-Shape Neural Networks
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Solution Overview
Problem
Conventional image text processing systems ineffectively identify the locations of text within images, particularly when text is rotated or inclined, and characters have variations in height, making it challenging to classify text strings accurately.
Innovation Solution
A deep neural network-based detection and localization system that uses region proposal networks to identify and classify text objects within images, employing a joint loss function to optimize both localization and classification simultaneously, and utilizing multiple shapes with different levels of precision to determine the orientation and position of text lines, with loss functions refining filter weights through backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image text processing systems are used, then the system structure is simple, but the text localization accuracy is poor especially for rotated or inclined text
Solution Approach 1:
The system segments the text detection task into multiple components: region proposal network for initial text region identification, multiple shape models (axis-aligned rectangle, rotated rectangle, polygon) for different text orientations, and selective loss functions for different shape types. This segmentation allows each component to specialize in specific aspects, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system changes the parameter representation from simple bounding boxes to multiple shape types with different parameters (axis-aligned rectangles with width/height, rotated rectangles with angle, polygons with vertex coordinates). This parameter change enables the system to accurately represent text in various orientations and configurations, directly addressing the limitation of conventional systems.
2Measurement precision
If conventional text detection methods are used, then the processing is fast, but the classification accuracy for text strings with character height variations is low
Solution Approach 1:
The system segments the text string into individual characters and analyzes each character's properties (height, width, position) separately. By examining character-level features and their variations, the system can accurately classify text strings even when characters have different heights, while maintaining efficient processing through parallel character analysis.
Solution Approach 2:
The system applies local quality analysis by examining the specific properties of each character within a text string (such as height variations) rather than treating the entire string uniformly. This local analysis enables accurate classification of text strings with varying character heights while maintaining processing efficiency through targeted feature extraction.
3Measurement precision
If multiple shape models with different precision levels are used, then the localization precision for different text orientations is improved, but the computational complexity increases
Solution Approach 1:
The system applies partial action by selectively applying different shape models and loss functions based on the specific needs of each text region. Rather than uniformly applying all shape models to all text, the system chooses the appropriate shape model (axis-aligned rectangle, rotated rectangle, or polygon) based on the text's orientation and characteristics, reducing unnecessary computational complexity while maintaining high precision where needed.
Solution Approach 2:
The system changes the computational approach by using different parameter representations for different shape types. Axis-aligned rectangles use simple width/height parameters, rotated rectangles add angle parameters, and polygons use vertex coordinates. This parameter change allows the system to efficiently represent complex text geometries while managing computational complexity through selective application of appropriate parameter sets.
Data Source
AI summary
A method and system for analyzing text in an image is disclosed. A text localization and classification system accesses an annotated image comprising a plurality of text location identifiers for a given item of text. A neural network predicts the location of the given item of text using at least a first location identifier and a second location identifier. Optionally, the first location identifier comprises a first shape and the second location identifier comprises a second shape. A first loss is generated using a first loss function, the first loss corresponding to the predicated location using the first location identifier. A second loss is generated using a second loss function, the second loss corresponding to the predicated location using the second location identifier. The neural network is enhanced with backpropagation using the first loss and the second loss.