Handwritten digit recognition by bio-inspired hierarchical networks

The biologically inspired Hierarchical Temporal Memory

It is herein proposed a handwritten digit recognition system which biologically inspired of the large-scale structure of the mammalian neocortex. Hierarchical Temporal Memory (HTM) is a memory-prediction network model that takes advantage of the Bayesian belief propagation and revision techniques. In this article a study has been conducted to train a HTM network to recognize handwritten digits ...

Persian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network

Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...

Application of Growing Hierarchical Self-Organizing Map in Handwritten Digit Recognition

This paper discusses the application of a GH-SOM architecture to the problem of Handwritten Digit Recognition. The results proved to be better than the ones obtained from standard SOM networks.

A Cortically Inspired Learning Model

We describe a biologically plausible learning model inspired by the structural and functional properties of the cortical columns present in the mammalian neocortex. The strength and robustness of our model is ascribed to its biologically plausible, uniformly structured, and hierarchically distributed processing units with their localized learning rules. By modeling cortical columns rather than ...

Recognition of Handwritten Persian Numbers and Arithmetic Symbols Using Neural Networks