Handwriting Recognition CNN with Cascaded Layers
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Solution Overview
Problem
Existing handwriting recognition systems face challenges in accurately transcribing historical records due to varying noise conditions, degraded document quality, and the complexity of handling different handwriting styles, leading to high costs and low accuracy, especially in large category and alpha-numeric fields.
Innovation Solution
A simplified handwriting recognition approach using a convolutional neural network (CNN) with max pooling layers and a cascade of concatenated convolution and dense neural network layers, which reduces the number of parameters and processing requirements, eliminating the need for time-distributed elements and complex decoding schemes, allowing for faster training and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks with CNN-RNN-CTC pipeline are used for handwriting recognition, then recognition capability is improved, but system complexity and number of parameters increase
Solution Approach 1:
The patent extracts and removes the RNN and CTC components from the traditional CNN-RNN-CTC pipeline, retaining only the CNN architecture. This simplification reduces system complexity while maintaining recognition capability by directly outputting character sequences from the CNN without requiring sequential processing or complex decoding schemes.
Solution Approach 2:
The patent segments the handwriting recognition task into independent character-level classification problems processed in parallel by the CNN, rather than using sequential RNN processing. This segmentation allows simultaneous processing of multiple characters, reducing temporal complexity while maintaining accuracy.
2Measurement precision
If LSTM or transformer-based models are used for handwriting recognition, then recognition capability is improved, but training time and computational resources increase
Solution Approach 1:
The patent replaces the sequential mechanical processing of RNN/LSTM with parallel CNN processing. Instead of processing characters one after another through time-distributed layers, the CNN processes all spatial positions simultaneously, dramatically reducing training time while maintaining recognition accuracy.
Solution Approach 2:
The patent transitions from temporal dimension processing (RNN/LSTM processing sequences step-by-step) to spatial dimension processing (CNN processing all characters simultaneously across the image). This dimensional shift enables parallel processing and reduces training time significantly.
3Measurement precision
If word beam search with language knowledge is used during decoding, then word completion accuracy is improved, but model vocabulary limitations and inability to handle spelling errors increase
Solution Approach 1:
The CNN model performs self-service by directly outputting character sequences without requiring external language models or beam search algorithms. This self-contained approach eliminates dependency on predefined vocabularies and language knowledge, allowing the model to handle spelling errors, new words, and variations independently through its character-level classification capability.
4Measurement precision
If manual transcription of historical records is performed, then accuracy can be maintained, but cost and time requirements become prohibitively high
Solution Approach 1:
The patent creates a digital copy of the manual transcription process through the CNN model. Instead of requiring actual human transcribers to read and transcribe historical records, the CNN learns to replicate the transcription task by processing images of handwritten text and outputting recognized character sequences, thereby maintaining accuracy while dramatically increasing productivity.
Data Source
AI summary
A simplified handwriting recognition approach includes a first network comprising convolutional neural network comprising one or more convolutional layers and one or more max-pooling layers. The first network receives an input image of handwriting and outputs an embedding based thereon. A second network comprises a network of cascaded convolutional layers including one or more subnetworks configured to receive an embedding of a handwriting image and output one or more character predictions. The subnetworks are configured to downsample and flatten the embedding to a feature map and then a vector before passing the vector to a dense neural network for character prediction. Certain subnetworks are configured to concatenate an input embedding with an upsampled version of the feature map.