Basic Research on Ancient Bai Character Recognition Based on Mobile APP

Abstract

Bai nationality has a long history and has its own language. Limited by the fact that there are fewer and fewer people who know the Bai language, the literature and culture of the Bai nationality begin to lose rapidly. In order to make the people who do not understand Bai characters can also read the ancient books of Bai nationality, this paper is based on the research of high-precision single character recognition model of Bai characters. First, with the help of Bai culture lovers and related scholars, we have constructed a data set of Bai characters, but limited by the need of expert knowledge, so the data set is limited in size. As a result, deep learning models with the nature of data hunger cannot get an ideal accuracy. In order to solve this issue, we propose to use the Chinese data set which also belongs to Sino-Tibetan language family to improve the recognition accuracy of Bai characters through transfer learning. In addition, we propose four transfer learning approaches: Direct Knowledge Transfer (DKT), Indirect Knowledge Transfer (IKT), Self-coding Knowledge Transfer (SCKT), and Self-supervised Knowledge Transfer (SSKT). Experiments show that our approaches greatly improve the recognition accuracy of Bai characters.

Variational Autoencoder for Zero-Shot Recognition of Bai Characters

“…Zhang et al [10] as a pioneer resorting to deep learning to recognize the Bai text collected a data set with 400 words of Bai handwritten fonts. In their work, Zhang et al [10] considered that the Chinese language is similar to Bai language to facilitate the model to recognize Bai characters based on learning transfer [10,16] and achieved remarkable results. However, their study is limited to a limited data set, and the data set contains only 400 Bai characters.…”

“…To this end, we consider using zero-shot learning (ZSL) [8,9] as collecting Bai character data sets is hard to obtain [10]. ZSL can transfer knowledge from visible categories to invisible ones according to their attributes to prevent samples of the invisible category from decreasing or even losing.…”

“…ZSL can transfer knowledge from visible categories to invisible ones according to their attributes to prevent samples of the invisible category from decreasing or even losing. If we treat Bai characters as unseen classes, we need to collect data sets similar to Bai characters as seen classes in order to better transfer knowledge [10]. Fortunately, it can be seen that Chinese and Bai characters are highly similar as they come from the Sino-Tibetan [11,12] language family, as shown in Figure 1.…”

“…Therefore, we collected a huge Chinese character data set as seen class in ZSL. Last but not least, ZSL needs to depict the attributes of both visible and invisible categories, so as to better transfer knowledge through attributes [10]. It is found that characters of Chinese and Bai are made up of 32 basic strokes, as shown in Figure 2.…”

“…After building the data set in ZSL format, we transplanted some classical methods in ZSL, which include projection methods: DAP [9] and IAP [9], and generation method- [13] based GAN [14]. These methods establish the relationship between attributes and features, so as to generalize Chinese characters to Bai characters and finally improve the accuracy [10]. However, our attributes only contain the stroke information of characters, so these models ignore the important information of character structure in the process of training.…”