@inproceedings{da1c8e04ee344b9dbf6987038178c2f3,
title = "Word spotting using convolutional siamese network",
abstract = "We present a method for word spotting using convolutional siamese network. A convolutional siamese network employs two identical convolutional network to rank similarity between two input word images. Once the network is trained, it can then be used to spot not just words with varying writing styles and backgrounds but also to spot out of vocabulary words that are not in the training set. Experiments on the historical Arabic manuscript dataset VML, and on the George Washington dataset shows comparable results with the state of the art.",
keywords = "Historical document image analysis, convolutional siamese network, deep learning, word spotting",
author = "Barakat, \{Berat Kurar\} and Reem Alasam and Jihad El-Sana",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 13th IAPR International Workshop on Document Analysis Systems, DAS 2018 ; Conference date: 24-04-2018 Through 27-04-2018",
year = "2018",
month = jun,
day = "22",
doi = "10.1109/DAS.2018.67",
language = "English",
series = "Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "229--234",
booktitle = "Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018",
address = "United States",
}