Attention-Based Neural Network for Handwritten Text Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text recognition techniques rely on line segmentation, which is not always available and can be expensive to obtain, leading to performance degradation and sensitivity to other markings in the image.
Innovation Solution
The system employs attention-based neural networks, specifically multi-dimensional long short-term memory (LSTM) recurrent neural networks, to recognize handwritten characters over multiple lines without explicit line segmentation, using attention weights and image summary vectors to predict characters iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If line segmentation is used to recognize text, then character recognition accuracy is improved, but processing complexity and computational cost increase
Solution Approach 1:
The patent extracts and removes the line segmentation step from the traditional text recognition pipeline. By directly processing the entire document image without segmenting into lines first, the system eliminates the complexity of line segmentation algorithms while maintaining recognition accuracy through attention mechanisms that focus on relevant character regions.
Solution Approach 2:
The patent merges the line segmentation function and character recognition function into a single unified process. The attention-based neural network simultaneously performs what would traditionally require separate segmentation and recognition steps, reducing overall processing complexity while achieving comparable or better accuracy.
2Ease of operation
If line segmentation algorithms are used, then text transcription is enabled, but the system becomes sensitive to other markings in the image
Solution Approach 1:
The patent employs dynamic attention mechanisms that adaptively adjust focus based on the content of each image region. Instead of rigidly segmenting based on assumed line structures, the attention mechanism dynamically identifies and focuses on character-like patterns while ignoring other markings, making the system robust to varied image contents.
Solution Approach 2:
The attention mechanism serves as an intermediary between the raw image input and the character recognition process. It selectively attends to relevant features while suppressing irrelevant markings, effectively filtering out harmful factors before they can interfere with transcription accuracy.
3Reliability
If complete processing pipeline with line segmentation is used, then text recognition is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and eliminates the time-consuming line segmentation preprocessing step from the processing pipeline. By feeding the entire document image directly into the attention-based neural network, the system reduces processing time while maintaining reliable text recognition through the attention mechanism's ability to focus on relevant regions.
Solution Approach 2:
The attention mechanism performs preliminary selective focus on character regions before full recognition processing. This allows the system to effectively preprocess the image by highlighting relevant areas, achieving reliable recognition without the need for explicit line segmentation preprocessing.
Data Source
AI summary
Methods and systems are provided for end-to-end text recognition in digitized documents of handwritten characters over multiple lines without explicit line segmentation. An image is received. Based on the image, one or more feature maps are determined. Each of the one or more feature maps include one or more feature vectors. Based at least in part on the one or more feature maps, one or more scalar scores are determined. Based on the one or more scalar scores, one or more attention weights are determined. By applying the one or more attention weights to each of the one or more feature vectors, one or more image summary vectors are determined. Based at least in part on the one or more image summary vectors, one or more handwritten characters are determined.