In this paper we present an innovative technique to semi-automatically index handwritten word images. The proposed method is based on HOG descriptors and exploits Dynamic Time Warping technique to compare feature vectors elaborated from single handwritten words. Our strategy is applied to a new challenging dataset extracted from Italian civil registries of the XIX century. Experimental results, compared with some previously developed word spotting strategies, confirmed that our method outperforms competitors.
CITATION STYLE
Bolelli, F., Borghi, G., & Grana, C. (2017). Historical handwritten text images word spotting through sliding window HOG features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10484 LNCS, pp. 729–738). Springer Verlag. https://doi.org/10.1007/978-3-319-68560-1_65
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