Unified Scene Text Detection With Attention-Based Layout Analysis
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Solution Overview
Problem
Existing technologies treat scene text detection and document layout analysis as separate tasks, lacking a unified approach that efficiently combines both functionalities without significant processing time or resource overhead.
Innovation Solution
A unified detector neural network that applies an attention mechanism across pixels and learned object queries to perform scene text detection and geometric layout analysis in a single model, enabling end-to-end processing without complex post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for scene text detection and document layout analysis, then each model can be optimized for its specific task, but the overall system complexity increases and processing time extends
Solution Approach 1:
The patent combines scene text detection and document layout analysis into a single unified neural network model. The model shares common feature extraction layers and uses a joint training approach with multi-task loss functions, allowing both tasks to be performed simultaneously within one system architecture, thereby reducing overall system complexity while maintaining task-specific performance
Solution Approach 2:
The unified neural network model is designed to perform multiple functions - both scene text detection and document layout analysis - within a single system. The model uses shared embeddings and attention mechanisms that can adapt to different task requirements, enabling one model to serve multiple purposes without sacrificing performance in either task
2Reliability
If separate models are used for scene text detection and document layout analysis, then each model can be independently trained, but the processing time and computational resources increase
Solution Approach 1:
By merging both tasks into a single unified model, the patent eliminates the need for sequential processing of separate models. The joint architecture processes both detection and layout analysis in parallel during a single forward pass, significantly reducing processing time while maintaining the performance benefits of task-specific optimization through shared feature representations
Solution Approach 2:
The unified model enables continuous processing where feature extraction, detection, and layout analysis occur in an uninterrupted pipeline. The shared computational layers continuously generate features that are simultaneously utilized by both task-specific heads, eliminating redundant computations and improving overall processing efficiency
3Productivity
If a unified model is used for both scene text detection and document layout analysis, then processing efficiency improves, but the model complexity increases
Solution Approach 1:
The unified model is segmented into distinct functional modules: shared feature extraction layers, task-specific detection heads, and layout analysis heads. This modular segmentation allows the model to handle multiple tasks while maintaining clear functional boundaries, making the complexity manageable and the architecture interpretable
Solution Approach 2:
Different parts of the unified model have specialized functions tailored to specific tasks. The detection heads are optimized for text localization while the layout heads focus on spatial relationships, allowing each component to have the appropriate complexity for its specific purpose while sharing common lower-level features
4Measurement precision
If standalone models are used for text detection and layout analysis, then each model can achieve high accuracy, but the overall system requires more computational resources
Solution Approach 1:
The patent merges computational resources by using shared feature extraction and embedding layers that serve both detection and layout analysis tasks. This eliminates redundant computations that would occur if separate models processed the same input data independently, reducing overall computational resource requirements while maintaining high accuracy through task-specific output layers
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly performing text detection and layout analysis. In one aspect, a method comprises processing the image and a set of object queries to generate an encoded representation of the image and an encoded representation of the set of object queries; processing the encoded representation of the image and the encoded representation of the set of object queries to generate a set of text detection masks; processing the encoded representation of the set of object queries to generate layout relevance measures; processing the encoded representation of the set of object queries to generate textness scores for the text detection masks; generating a text detection output that defines respective areas of the image that include text items; and generating a layout analysis output that defines clusters of respective areas of the image identified by the text detection masks.