Handwriting Recognition Model Merging Detection and Recognition
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Solution Overview
Problem
Current handwriting recognition methods for full text are limited by the detection performance of detectors, requiring separate data labeling and model training for detection and recognition, and have complex network structures that complicate the implementation process.
Innovation Solution
A handwriting recognition method that includes an image feature extraction layer, a full connection layer, and a Softmax layer, with a height compression module, which extracts feature maps, adjusts channel numbers, and outputs prediction probability values for spatial positions, allowing for multi-neighborhood merging to obtain recognition results, and a training method that uses a single-line sample text image for model training, reducing the need for complex data annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a two-stage text detection and recognition approach is used, then recognition functionality is achieved, but the system complexity increases and detection performance limits overall recognition performance
Solution Approach 1:
The patent merges text detection and recognition into a single integrated model that processes text traces directly without separate detection and recognition stages. The model combines feature extraction, position prediction, and character recognition in one unified architecture, eliminating the need for separate detectors and reducing overall system complexity while maintaining high recognition accuracy.
Solution Approach 2:
The integrated model performs multiple functions simultaneously - it extracts features from text traces, detects text positions, and recognizes characters all within a single model framework. This multi-functional approach replaces the traditional separate detection and recognition systems, reducing the number of components needed while improving overall system efficiency.
2Reliability
If separate data labeling and model training are performed for detection and recognition, then specialized performance is achieved, but the implementation process becomes tedious
Solution Approach 1:
The patent combines detection and recognition training into a single unified training process where one model learns to perform both detection and recognition tasks. This eliminates the need for separate data labeling and training pipelines, significantly simplifying the implementation process while maintaining specialized performance for both functions through the integrated architecture.
3Adaptability or versatility
If an end-to-end multi-line recognition network is used, then recognition functionality is integrated, but the network structure becomes complex
Solution Approach 1:
The patent segments the recognition task into distinct functional components within a simplified architecture: feature extraction module, position prediction module, and character recognition module. This segmentation allows each component to focus on a specific function while maintaining overall simplicity, avoiding the complexity of traditional end-to-end multi-line recognition networks.
Data Source
AI summary
A handwriting recognition method including: determining an input text image according to a written text trace to be recognized; inputting the input text image into a handwriting recognition model to obtain prediction results of different spatial positions in the input text image. The handwriting recognition model includes an image feature extraction layer, a full connection layer and a Softmax layer, the image feature extraction layer is used for extracting a feature map of the input text image, the full connection layer is used for adjusting the number of the channels of the feature map to the number of characters supported by the handwriting recognition model, and the Softmax layer is used for obtaining the prediction probability values of the written text at different spatial positions; performing a multi-neighborhood merging on the prediction results of different spatial positions to obtain a recognition result.