A pattern recognition based Holographic Graph Neuron for Persian alphabet recognition
Paper ID : 1110-MVIP2020 (R1)
Abdorreza Alavi gharahbagh *, Vahid Hajihashemi, Mohammad Mehdi Arab Ameri, Azam Bastanfard
In this article a Vector Symbolic Architectures is purposed to implement a hierarchical Graph Neuron for memorizing patterns of Persian/Arabic isolated characters. The main challenge in this topic is using Vector Symbolic representation as a one-layered design for neural network while maintaining the previously reported properties and performance characteristics of hierarchical Graph Neuron. The designed architecture is robust to noise and enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern. The proposed method was implemented on a standard Persian database and the obtained results showed the ability of (not necessarily better) Graph neuron to recognize the Persian isolated character patterns.
HoloGN, Neural Network, OCR, Graph neuron, Holographic
Status : Paper Accepted (Oral Presentation)