Oracle Character Recognition by Nearest Neighbor Classification with Deep Metric Learning

• Main Authors: Zhang, Yi-Kang, Zhang, Heng, Liu, Yong-Ge, Yang, Qing, Liu, Cheng-Lin
• Format: Conference Proceeding
• Language: English
• Subjects: Acceleration; Artificial neural networks; Character recognition; deep metric learning; dense convolutional network; Measurement; nearest-neighbor classifier; Network architecture; oracle character recognition; Prototypes; Training
• ISSN: 2379-2140
• DOI: 10.1109/ICDAR.2019.00057

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU.