CSRS: A Chinese Seal Recognition System with Multi-Task Learning and Automatic Background Generation

Abstract

As an important part of the Chinese painting and calligraphy, the seals not only have a high value of art but also contain a lot of information about the artwork itself. At this digital age, we would like not only be able to represent the seals in the digital format, but also like to use image processing techniques to help us better understand them. With the development of deep learning, convolutional neural networks have been widely used in the fields of feature learning, object localization, and classification. Based on deep learning technology, this paper proposes a highly accurate Chinese seal recognition system (CSRS). With our CSRS, users could simply input a single seal image into the system, then CSRS would automatically recognize the seal and report the relevant information in real-time. The CSRS mainly contains three units. 1) A new Siamese network with multi-task learning (Siamese-MTL), which can effectively solve the similarity measurement problem and improve the generalization of the model. 2) A new online data generation algorithm called automatic background generation (ABG) which could generate numerous seal images with different backgrounds for effective training. 3) A new training method for Siamese network which based on a central constraint. In order to validate the effectiveness of the proposed method, we have established two large scale seals image databases, including 15,000 Chinese seal images and 1,700 background images, respectively. We evaluate our method and compare with the variant methods on these datasets, achieving the highest performance. The extensive experimental results indicate that our proposed method is effective and has a great potential for the practical application in Chinese seals recognition.