Neural Network Handwriting Recognition for Irregular Writing Orders
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Solution Overview
Problem
Existing online handwriting text recognition systems suffer from low recognition ratios and struggle to accurately recognize natural languages, including those written from right to left, and cannot handle texts written in irregular orders.
Innovation Solution
A neural network-based handwriting text recognition system that includes a stroke input processor, a string database, and a word recognition neural network with an encoder and decoder structure, capable of processing strokes and producing confident predictions for character strings, even with irregular writing orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional separation and decoding method or two-direction circulation neural network is used, then the system can be implemented, but the recognition ratio remains low and cannot accurately recognize many natural languages including right-to-left scripts
Solution Approach 1:
The patent segments the character string recognition task into multiple character-level predictions. The neural network predicts each character position independently, allowing the system to handle variable-length inputs and diverse writing orders. This segmentation enables the system to process different language scripts (including right-to-left) by treating each character position as a separate prediction target rather than requiring fixed-order processing.
Solution Approach 2:
The patent introduces a new dimensional approach by adding character position as an explicit output dimension. Instead of predicting characters in a fixed sequence, the system outputs both character predictions and their corresponding positions. This dimensional transformation allows the model to handle irregular writing orders and multiple language scripts simultaneously, resolving the contradiction between recognition accuracy and language versatility.
2Adaptability or versatility
If the system uses fixed-order stroke processing, then implementation is simpler, but it cannot recognize texts written in irregular orders
Solution Approach 1:
The patent implements dynamic character position prediction where the system adapts to different writing orders rather than enforcing a fixed sequence. The neural network learns to predict character positions dynamically based on the input stroke sequence, allowing it to handle both regular and irregular writing orders. This dynamic approach increases versatility while keeping the system architecture relatively simple through end-to-end training.
3Adaptability or versatility
If more training data and languages are added to improve coverage, then the system becomes more complex and harder to train
Solution Approach 1:
The patent creates a universal character-level prediction framework that can handle multiple languages and writing orders through a single unified model. By predicting characters at different positions rather than enforcing language-specific processing sequences, the system achieves multi-functionality across different language scripts. This universal approach allows adding new languages to the training data without fundamentally changing the system architecture or training complexity.
Data Source
AI summary
A handwriting text recognition system based on neural network includes a stroke input processor for receiving tracks of online handwriting texts, a string database for storing a large amount of the tracks; a word recognition neural network; and an after-processor being connected to the string database and the output interface of the text recognition neural network; The handwriting text recognition system based on neural network provides higher rates of confidences. Some natural languages frequently used all over the world can be recognized with a higher accuracy (including languages written from right to left and from left to right). The association relations between the input strokes and the character strings can be provided. It could support any strokes with irregular written orders.