Key-Guided Identity Document Classification Method by Graph Attention Network

Identify documents, such as personal identity cards, driving licenses, passports, and residence permits, are indispensable and necessary in human life. Various industries, like hotel accommodation, banking service, traffic service and car renting, need identify documents to extract and verify the personal information. Automatic identity document classification can help people to quickly acquire the valid information from the documents, saving the labor cost and improving the efficiency. However, due to the inconsistency and diversity of identify documents between different countries, automatic classification of these documents is still a challenging problem. We propose an identity document classification method in this paper. GAT (Graph Attention Network) and its edge-based variant are exploited to generate the graph embedding, which fuse visual features and textual features together. Moreover, the key information is learnt to guide to a better representation of the document feature. Extensive experiments on the identify document dataset have been conducted to prove the effectiveness of the proposed method.