Re-Ranking for Writer Identification and Writer Retrieval

Simon Jordan and Mathias Seuret and Pavel Král and Ladislav Lenc and Jiří Martínek and Barbara Wiermann and Tobias Schwinger and Andreas Maier and Vincent Christlein
14th IAPR International Workshop on Document Analysis Systems (2020)



Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets.



@misc{, doi = {10.48550/ARXIV.2007.07101}, url = {}, author = {Jordan, Simon and Seuret, Mathias and Král, Pavel and Lenc, Ladislav and Martínek, Jiří and Wiermann, Barbara and Schwinger, Tobias and Maier, Andreas and Christlein, Vincent}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Re-ranking for Writer Identification and Writer Retrieval}, publisher = {arXiv}, year = {2020}, copyright = { perpetual, non-exclusive license} }
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