Image Based HTM Word Recognizer for Language processing [Article]

James, Alex Pappachen; Irmanova, Aidana; Krestinskaya, Olga

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dc.contributor.author	James, Alex Pappachen
dc.contributor.author	Irmanova, Aidana
dc.contributor.author	Krestinskaya, Olga
dc.date.accessioned	2019-09-26T06:49:32Z
dc.date.available	2019-09-26T06:49:32Z
dc.date.issued	2019-11-29
dc.identifier.citation	Irmanova, A., Krestinskaya, O., & James, A. P. (2018). Image Based HTM Word Recognizer for Language Processing. In 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE. https://doi.org/10.1109/icce-asia.2018.8552117	en_US
dc.identifier.isbn	978-1-5386-5807-9
dc.identifier.other	10.1109/ICCE-ASIA.2018.8552117
dc.identifier.uri	http://nur.nu.edu.kz/handle/123456789/4261
dc.description	https://ieeexplore.ieee.org/document/8552117	en_US
dc.description.abstract	The hardware implementation of neuro-inspired machine learning algorithms for near sensor processing on edge devices is an open problem. In this work, we propose a solution to written word recognition problem related to sequence learning tasks with images. Applying a theoretical framework of neocortex functionality as a sequence learning algorithm on a hardware implementation of Hierarchical Temporal Memory (HTM), we test the potential use of HTM in near-sensor on-chip natural language processing for text/symbol recognition.	en_US
dc.language.iso	en	en_US
dc.publisher	Institute of Electrical and Electronics Engineers	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/us/	*
dc.subject	HTM Word Recognizer	en_US
dc.subject	Language Processing	en_US
dc.subject	Hierarchical Temporal Memory	en_US
dc.subject	Research Subject Categories::TECHNOLOGY	en_US
dc.title	Image Based HTM Word Recognizer for Language processing [Article]	en_US
dc.type	Article	en_US
workflow.import.source	science