Content and Style Aware Generation of Text-Line Images for Handwriting Recognition.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Handwritten Text Recognition (HTR) systems require vast labeled datasets due to handwriting variability.
• Current synthetic data generation using TrueType fonts introduces a style bias, limiting HTR performance.
• Addressing the need for diverse and realistic training data is crucial for advancing HTR.

Purpose of the Study:

• To propose a novel generative method for creating realistic handwritten text-line images.
• To overcome the limitations of existing synthetic data generation techniques, specifically the style bias.
• To improve the performance of Handwritten Text Recognition systems by leveraging generated data.

Main Methods:

• A conditional generative method is proposed, utilizing both visual appearance and textual content.
• The method generates long text-line samples with diverse handwriting styles.
• The trained model can adapt to new target data using unlabeled images to mimic specific handwriting styles.

Main Results:

• The generative method produces diverse, long text-line samples that closely mimic real handwriting.
• Experiments demonstrate that using the generated samples significantly boosts Handwritten Text Recognition performance.
• Both qualitative and quantitative evaluations confirm the superiority of the proposed approach over the state-of-the-art.

Conclusions:

• The proposed generative method effectively addresses the style bias in synthetic data for HTR.
• This approach offers a flexible way to generate high-quality, style-specific training data for HTR.
• The method represents a significant advancement in improving HTR accuracy and efficiency.