A-VLAD: An End-to-End Attention-Based Neural Network for Writer Identification in Historical Documents

This paper presents an end-to-end attention-based neural network for identifying writers in historical documents. The proposed network does not require any preprocessing stages such as binarization or segmentation. It has three main parts: a feature extractor using Convolutional Neural Network (CNN) to extract features from an input image; an attention filter to select key points; and a generalized deep neural VLAD model to form a representative vector by aggregating the extracted key points. The whole network is trained end-to-end by a combination of cross-entropy and triplet losses. In the experiments, we evaluate the performance of our model on the HisFragIR20 dataset that consists of about 120,000 historical fragments from many writers. The experiments demonstrate better mean average precision and accuracy at top-1 in comparison with the state-of-the-art results on the HisFragIR20 dataset. This model is rather new for dealing with various sizes of historical document fragments in the writer identification and image retrieval.