Zero-shot learning based approach for medieval word recognition using deep-learned featuresChanda, S., Baas, J., Haitink, D., Hamel, S., Stutzmann, D. & Schomaker, L., 5-Dec-2018, Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR. Institute of Electrical and Electronics Engineers Inc., p. 345-350 6 p. (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; vol. 2018-August).
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
Historical manuscripts reflect our past. Recently digitization of large quantities of historical handwritten documents is taking place in every corner of the world, and are being archived. From those digital repositories, automatic text indexing and retrieval system fetch only those documents to an end user that they are interested in. A regular OCR technology is not capable of rendering this service to an end user in a reliable manner. Instead, a word recognition/spotting algorithm performs the task. Word recognition based systems require enough labelled data per class to train the system. Moreover, all word classes need to be taught beforehand. Though word spotting could evade this drawback of prior training, these systems often need to have additional overheads like a language model to deal with "out of lexicon" words. Zero-shot learning could be a possible alternative to counter such situation.
|Title of host publication||Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 5-Dec-2018|
|Name||Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR|
- Out of lexicon word recognition, Zero shot learning for word recogntion