Fully Convolutional Neural Network for Handwriting Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital character recognition systems, particularly for handwritten text, face challenges in accurately processing diverse writing styles and handling arbitrary length symbols due to limitations in feature extraction and alignment, leading to suboptimal performance with unbounded dictionaries and distortions in offline handwriting recognition.
Innovation Solution
The implementation of a Fully Convolutional Neural Network (FCNN) architecture that normalizes input blocks to a canonical representation, utilizes multi-stream contextual filters, and introduces a probabilistic character error rate for correction, enabling accurate symbol recognition and classification without relying on predefined dictionaries or prior segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional OCR methods are used for handwritten text recognition, then processing is simpler, but recognition accuracy deteriorates due to infinite writing style variations
Solution Approach 1:
The patent replaces traditional mechanical OCR systems with neural network-based systems. Specifically, it uses convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) with long short-term memory (LSTM) units for sequence recognition, substituting rule-based mechanical processing with adaptive learning-based processing to handle writing style variations.
Solution Approach 2:
The patent changes the approach from fixed-rule parameter processing to dynamic parameter learning. The neural networks automatically learn optimal parameters for feature extraction and recognition from training data, adapting to different writing styles without manual parameter adjustment, thereby improving reliability across diverse handwriting.
2Reliability
If neural networks are used to improve handwritten text recognition, then recognition accuracy improves, but processing intensity increases significantly
Solution Approach 1:
The patent segments the recognition process into distinct stages: line segmentation separates individual lines of text, word segmentation divides lines into words, and character-level recognition processes each word. This segmentation allows the system to apply appropriate processing intensity at each stage, reducing overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent employs a two-stage recognition approach where a first neural network performs initial recognition and a second neural network performs correction on uncertain cases. This partial application of intensive processing only where needed reduces overall processing intensity while maintaining high recognition accuracy.
3Measurement precision
If precise alignment is required for symbol recognition, then recognition precision improves, but handling of arbitrary length symbols deteriorates
Solution Approach 1:
The patent uses dynamic sequence processing with RNNs and LSTMs that can adapt to variable-length symbol sequences. The recurrent architecture naturally handles arbitrary lengths by processing symbols sequentially, maintaining precision for each symbol while adapting to any sequence length without requiring fixed alignment.
Solution Approach 2:
The patent introduces attention mechanisms as intermediaries between feature extraction and recognition. The attention mechanism dynamically weights important symbols regardless of their position, enabling precise recognition of key symbols in arbitrary-length sequences without requiring strict global alignment.
4Measurement precision
If dictionary-based recognition is used, then common words are recognized accurately, but unbounded dictionaries (phone numbers, surnames, addresses) cannot be handled
Solution Approach 1:
The patent creates a universal recognition system that handles both bounded dictionaries (common words) and unbounded dictionaries (phone numbers, surnames, addresses) through the same neural network architecture. The system automatically adapts to different symbol sequences without requiring pre-programmed dictionaries for each category, achieving multi-functionality.
Solution Approach 2:
The neural network system performs self-learning from training data to recognize patterns in both common words and arbitrary symbol sequences. Instead of requiring external dictionary provisioning for each symbol type, the system autonomously learns to handle diverse symbol sets including unbounded dictionaries through exposure to varied training examples.
Data Source
AI summary
Embodiments of the present disclosure include a method for extracting symbols from a digitized object. The method includes processing the word block against a dictionary. The method includes comparing the word block against a word in the dictionary, the comparison providing a confidence factor. The method includes outputting a prediction equal to the word when the confidence factor is greater than a predetermined threshold. The method includes evaluating properties of the word block when the confidence factor is less than the predetermined threshold. The method includes predicting a value of the word block based on the properties of the word block. The method further includes determining an error rate for the predicted value of the word block. The method includes outputting a value for the word block, the output equal to a calculated value corresponding to a value of the word block having the lowest error rate.