Date of Award
Bachelor of Science
Despite significant advancement in Optical Character Recognition (OCR), Handwritten Hangul (Korean) Recognition, abbreviated as HHR, remains largely unsolved due to the similarity found in Hangul handwriting and a much larger number of syllables (∼ 11, 000) to classify compared to English or Latin script languages. The state-of-the-art approach toward HHR on the SERI95a handwritten Korean dataset was based on the AlexNet trained with data augmentation and hybrid learning. While this method has achieved a higher classification performance than the traditional approaches, it is limited in learning feature representations at various levels. In this study, we adopt a variation of Xception which exploits depthwise separable convolutions to enhance the recognition rate of individual syllables. In addition, we propose a progressive training method from Progressive Growing GAN to provide a stabilized training process with reduced training time. Through this approach, the empirical results demonstrate that the proposed approach outperforms the state-of-the-art method and demonstrates its effectiveness on HHR.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License
Cho, Joshua, "Handwritten Hangul Recognition" (2023). Computer Science. 7.