Handwritten Text GAN Translation for Accurate OCR
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Solution Overview
Problem
Existing handwriting recognition systems struggle with challenges such as varying noise conditions, interfering annotations, fading and vanishing texts, and variations in handwriting, making it difficult to transcribe handwritten documents accurately and efficiently.
Innovation Solution
A novel conditional sliced Wasserstein generative adversarial network (GAN) with a generator, word-level discriminator, and character-level discriminator is used to transform illegible handwritten text images into machine-print form, leveraging a U-Net architecture and sliced Wasserstein distance to capture low-level and abstract features, and enforce Lipschitz continuity through gradient penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR models are used for handwriting recognition, then machine print text recognition is successful, but handwriting recognition accuracy deteriorates due to noise, annotations, fading, and character connection challenges
Solution Approach 1:
The patent segments the handwriting recognition task into multiple stages: first transforming the handwritten image into a standardized format, then performing character recognition. The generator network divides the complex recognition problem into sub-problems by separately handling noise removal, character separation, and text transformation, allowing each component to be optimized independently for better overall accuracy
Solution Approach 2:
The patent introduces an intermediary transformation step between the handwritten input and the OCR recognition process. The generator network acts as a mediator that converts diverse handwriting variations into a standardized intermediate representation, making the subsequent recognition task more reliable and accurate
2Ease of operation
If sequential networks with CTC loss are used for handwriting recognition, then character-level annotation challenges are avoided, but post-processing requirements and recognition accuracy for connected characters deteriorate
Solution Approach 1:
The patent performs preliminary character separation and text transformation before the recognition process. The generator network pre-processes the handwritten image by separating connected characters and standardizing the text layout, so that the subsequent OCR model can operate on already-separated characters without requiring complex post-processing or character-level annotations
3Manufacturing precision
If GAN models are used for image generation, then realistic image generation is achieved, but training difficulty increases due to vanishing gradient, mode collapsing, and Nash equilibrium issues
Solution Approach 1:
The patent implements a feedback mechanism where the discriminator network provides continuous feedback to the generator network during training. The discriminator evaluates the realism of generated images and guides the generator to improve, creating a feedback loop that stabilizes training and prevents mode collapsing by continuously adjusting the generation process based on evaluation results
Solution Approach 2:
The patent employs parameter changes in the loss function and network architecture to simplify training. By modifying the objective function and adjusting network parameters dynamically during training, the system achieves stable convergence and reduces the complexity of reaching Nash equilibrium while maintaining high image generation quality
4Productivity
If handwritten text images are directly processed by OCR, then processing speed is maintained, but transcription accuracy deteriorates due to illegibility, noise, and fading
Solution Approach 1:
The patent performs preliminary image transformation and enhancement before OCR processing. The generator network pre-processes the handwritten image by removing noise, enhancing legibility, and standardizing the text format, so that the subsequent OCR recognition can proceed quickly with high accuracy without requiring multiple retry attempts
Data Source
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AI summary
Described herein are systems, methods, and other techniques for training a generative adversarial network (GAN) to perform an image-to-image transformation for recognizing text. A pair of training images are provided to the GAN. The pair of training images include a training image containing a set of characters in handwritten form and a reference training image containing the set of characters in machine-recognizable form. The GAN includes a generator and a discriminator. The generated image is generated using the generator based on the training image. Update data is generated using the discriminator based on the generated image and the reference training image. The GAN is trained by modifying one or both of the generator and the discriminator using the update data.