PT Unknown AU Mohammed Al Rawi Ernest Valveny Dimosthenis Karatzas TI Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? BT 15th International Conference on Document Analysis and Recognition PY 2019 BP 260 EP 267 DI 10.1109/ICDAR.2019.00050 AB 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. ER