Handwritten Digits 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...

Handwritten digit recognition using biologically inspired features

Image recognition problems are usually difficult to solve using raw pixel data. To improve the recognition it is often needed some form of feature extraction to represent the data in a feature space. We use the output of a biologically inspired model for visual recognition as a feature space. The output of the model is a binary code which is used to train a linear classifier for recognizing han...

Recognition of Handwritten Persian Numbers and Arithmetic Symbols Using Neural Networks

Recognition of Handwritten Digits Using Multilayer Perceptrons

Neural networks are often used for pattern recognition. They prove to be a popular choice for OCR (Optical Character Recognition) systems, especially when dealing with the recognition of printed text. In this paper, multilayer perceptrons are used for the recognition of handwritten digits. The accuracy achieved proves that this application is a working prototype that can be further extended int...