國立政治大學統計學系
學 術 演 講
學 術 演 講
主講人：葉倚任 副教授(國立高雄師範大學數學系)
題 目：Recent Advances in Text Recognition
時 間：民國113年12月30日 (星期一) 下午1：30
地 點：國立政治大學逸仙樓050101教室
摘 要：
Scene text recognition (STR) has been widely studied in academia and industry. Training a text recognition model often requires a large amount of labeled data, but data labeling can be difficult, expensive, or time-consuming, especially for Traditional Chinese text recognition. We present a framework for a Traditional Chinese synthetic data engine which aims to improve text recognition model performance. However, recognizing text from real-world images still faces challenges due to the domain shift between synthetic and real-world text images. One strategy to eliminate this domain difference without manual annotation is unsupervised domain adaptation (UDA). Due to the characteristics of sequential labeling tasks, most popular UDA methods cannot be directly applied to text recognition. To tackle this problem, we proposed a UDA method that minimizes latent entropy on sequence-to-sequence attention-based models with class-balanced self-paced learning. Experimental results show that our proposed framework achieves better recognition results than the existing methods on most UDA text recognition benchmarks.
