Neural Networks for Handwritten English Alphabet Recognition

05/17/2012

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This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.

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Neural Networks for Handwritten English Alphabet Recognition

Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

Handwritten Character Recognition Using Unique Feature Extraction Technique

Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks

English Sentence Recognition using Artificial Neural Network through Mouse-based Gestures

Combinational neural network using Gabor filters for the classification of handwritten digits

A Dataset of Inertial Measurement Units for Handwritten English Alphabets

Counting the learnable functions of structured data

Neural Networks for Handwritten English Alphabet Recognition

Related Research

Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

Handwritten Character Recognition Using Unique Feature Extraction Technique

Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks

English Sentence Recognition using Artificial Neural Network through Mouse-based Gestures

Combinational neural network using Gabor filters for the classification of handwritten digits

A Dataset of Inertial Measurement Units for Handwritten English Alphabets

Counting the learnable functions of structured data