Data Augmentation with Automatically Generated Images for Character Classifier Model Training

This paper presents a novel data augmentation technique crucial for training AI-OCR systems for handwritten character classification. Using a Y-autoencoder (Y-AE) enhanced with Adaptive Instance Normalization, diverse handwriting styles are generated to improve the breadth of handwriting representations. A filtering mechanism is introduced to include only valid character images for training. The method was tested on a subset of the ETL Character Database, featuring 92 unique Japanese Hiragana and Katakana characters. The baseline classifier achieved an accuracy of 0.9061. However, when using the augmented dataset, which included Y-AE model-generated and filtered images, the accuracy improved to a maximum 0.9555 with data augmentation technique. These results showcase the potential of this data augmentation technique in consumer electronics, particularly in AI-OCR software. Despite needing some noise removal, the approach significantly boosts classifier accuracy, suggesting an efficient way forward for document processing in various sectors.