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The issue of writer identification and writer retrieval, which is considered a challenging problem in the field of document analysis and recognition is addressed here.. A novel pipeline is proposed for the problem at hand by employing a unified neural network architecture consisting of the ResNet‐20 as a feature extractor and an integrated NetVLAD layer, inspired by the vector of locally aggregated descriptors (VLAD), in the head of the latter part. Having defined this architecture, the triplet semi‐hard loss function is used to directly learn an embedding for individual input image patches. Subsequently, the generalised max‐pooling technique is employed for the aggregation of embedded descriptors of each handwritten image. Also, a novel re‐ranking strategy is introduced for the task of identification and retrieval based on the  k ‐reciprocal nearest neighbours, and it is shown that the pipeline can benefit tremendously from this step. Experimental evaluation has been done on the three publicly available datasets: the ICDAR 2013, CVL, and KHATT datasets. Results indicate that while the performance was comparable to the state‐of‐the‐art KHATT, the writer identification and writer retrieval pipeline achieve superior performance on the ICDAR 2013 and CVL datasets in terms of mAP.

Writer identification and writer retrieval based on NetVLAD with Re‐ranking