Image Based HTM Word Recognizer for Language processing [Article]

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James, Alex Pappachen
Irmanova, Aidana
Krestinskaya, Olga

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Institute of Electrical and Electronics Engineers

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.

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https://ieeexplore.ieee.org/document/8552117

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

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Except where otherwised noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States