TY  - CONF
AU  - Mohammed Al Rawi
AU  - Ernest Valveny
AU  - Dimosthenis Karatzas
A2  - ICDAR
PY  - 2019//
TI  - Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting?
BT  - 15th International Conference on Document Analysis and Recognition
SP  - 260
EP  - 267
N2  - 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.
UR  - https://ieeexplore.ieee.org/abstract/document/8978043
L1  - http://refbase.cvc.uab.es/files/RVK2019.pdf
UR  - http://dx.doi.org/10.1109/ICDAR.2019.00050
N1  - DAG; 600.129; 600.121
ID  - Mohammed Al Rawi2019
ER  -