Fine-grained classification of identity document types with only one example

Abstract

In this paper, we tackle the task of recognizing types of partly very similar identity documents using state-of-the-art visual recognition approaches. Given a scanned document, the goal is to identify the country of issue, the type of document, and its version. Whereas recognizing the individual parts of a document with known standardized layout can be done reliably, identifying the type of a document and therefore also its layout is a challenging problem due to the large variety of documents. In our paper, we develop and evaluate different techniques for this application including feature representations based on recent achievements with convolutional neural networks. On a dataset with 74 different classes and using only one training image per class, our best approach achieves a mean class-wise accuracy of 97.7%.