會議論文
| 學年 | 114 |
|---|---|
| 學期 | 2 |
| 發表日期 | 2026-05-27 |
| 作品名稱 | Cross-domain Generalization of YOLO-based Models for Large-scale Cursive Chinese Character Classification |
| 作品名稱（其他語言） | |
| 著者 | Ching Teng; Chii-Jen Chen |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | The International Conference on Recent Advancements in Computing in AI, IoT and Computer Engineering Technology (CICET 2026) |
| 會議地點 | New Taipei, Taiwan |
| 摘要 | This study investigates cross-domain generalization in large-scale cursive Chinese character classification under domain shift. A dataset of 985 character classes is constructed by manually cropping glyph images from cursive calligraphy copybooks. The data are divided into train/validation/test sets for in-domain evaluation, together with a cross-source wild dataset (N = 25,610) that simulates variations in color, illumination, and background conditions. We evaluate YOLO-based classification models with different capacities and systematically control color augmentation intensity (Base, M1, S1) to analyze the interaction between model capacity and robustness. Although all models achieve near-saturated in-domain performance (Top-1 = 1.000), substantial differences emerge on the wild dataset. The highest Wild Top-1 accuracy is achieved by YOLOv8s under Base augmentation (0.9972), while lightweight models exhibit stronger sensitivity to augmentation strategies. Experimental results show that the effectiveness of color augmentation is architecture-dependent. Moderate augmentation improves lightweight models, whereas excessive perturbation may degrade performance. These findings highlight the importance of explicit cross-domain evaluation and provide practical insights for designing robust cursive glyph recognition systems. |
| 關鍵字 | Cursive Chinese character classification;cross-domain generalization;YOLO-based classification;data augmentation;domain shift. |
| 語言 | en_US |
| 收錄於 | |
| 會議性質 | 國內 |
| 校內研討會地點 | 淡水校園 |
| 研討會時間 | 20260527~20260529 |
| 通訊作者 | Chii-Jen Chen |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129947 ) |