Image Based HTM Word Recognizer for Language Processing

Aidana Irmanova, Olga Krestinskaya, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538658079
StatePublished - Nov 28 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23


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