%0 Conference Proceedings
%T Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting?
%A Mohammed Al Rawi
%A Ernest Valveny
%A Dimosthenis Karatzas
%B 15th International Conference on Document Analysis and Recognition
%D 2019
%F Mohammed Al Rawi2019
%O DAG; 600.129; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3337), last updated on Tue, 25 Jan 2022 16:37:10 +0100
%X Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language.
%U https://ieeexplore.ieee.org/abstract/document/8978043
%U http://refbase.cvc.uab.es/files/RVK2019.pdf
%U http://dx.doi.org/10.1109/ICDAR.2019.00050
%P 260-267