Fully Convolutional Neural Network for Handwriting Recognition
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Solution Overview
Problem
Current digital character recognition methods for handwritten text, particularly those using neural networks, face challenges in handling the infinite variations in handwriting styles and require sophisticated feature extraction and alignment processes, leading to suboptimal performance.
Innovation Solution
The implementation of a Fully Convolutional Neural Network (FCNN) architecture that processes digitized images by forming line blocks, transforming them into word blocks, and utilizing a hierarchical FCNN for symbol recognition, along with probabilistic correction to handle variations in handwriting, without relying on predefined dictionaries or contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR methods are used for handwritten text recognition, then processing simplicity is maintained, but recognition accuracy deteriorates due to infinite handwriting style variations
Solution Approach 1:
The patent replaces traditional mechanical feature extraction and alignment systems with a Fully Convolutional Neural Network that automatically learns features from raw pixel inputs. The FCNN substitutes complex preprocessing pipelines (including segmentation, feature extraction, and alignment steps) with a single end-to-end trainable model that directly maps images to character predictions, eliminating the need for manual feature engineering while achieving superior recognition accuracy.
2Measurement precision
If recurrent neural networks with alignment correction are used, then recognition accuracy improves, but processing time and computational cost increase significantly
Solution Approach 1:
The patent extracts and removes the recurrent temporal processing component from the architecture. By using purely convolutional layers without recurrence, the model eliminates the need for sequential processing and backpropagation through time, thereby reducing computational overhead and processing time while maintaining recognition accuracy through parallel processing capabilities of convolutional operations.
Solution Approach 2:
The FCNN performs feature extraction and alignment corrections in advance during the forward pass, integrating these operations into the main processing stream rather than requiring separate post-processing alignment steps. The network learns to predict character positions and orientations directly from the image features, performing alignment corrections preliminarily within the convolutional layers themselves.
3Productivity
If dictionary-based recognition systems are used, then processing speed is maintained, but adaptability to arbitrary symbols and names deteriorates
Solution Approach 1:
The patent implements a universal recognition system that can handle multiple character types (alphabetic, numeric, symbolic) and arbitrary vocabulary without requiring separate processing pipelines. The FCNN architecture processes all input types uniformly through the same convolutional layers, enabling the system to recognize both common dictionary words and arbitrary symbols, names, and specialized notations with equal effectiveness, thereby achieving both speed and versatility.
Data Source
AI summary
Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.