Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00050 | 2019 International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
Scene text images, Handwriting, Histogram of Characters, PHOC, Multitasking, ResNet | Histogram,Embedding,Pattern recognition,Handwriting,Computer science,Textual information,Bengali,Natural language processing,Artificial intelligence,Deep learning,Human multitasking,Spotting | Conference |
ISSN | ISBN | Citations |
1520-5363 | 978-1-7281-3015-6 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Al-Rawi | 1 | 0 | 0.34 |
| Ernest Valveny | 2 | 647 | 41.65 |
| Dimosthenis Karatzas | 3 | 406 | 38.13 |