English Character Recognition using Artificial Neural Network
This is an incremental improvement for character recognition systems, addressing a domain-specific problem in pattern recognition.
The paper tackled offline handwritten English character recognition by developing an algorithm based on an artificial neural network, achieving a maximum accuracy of 92.59% and a false rejection rate of 0% in tests.
This work focuses on development of a Offline Hand Written English Character Recognition algorithm based on Artificial Neural Network (ANN). The ANN implemented in this work has single output neuron which shows whether the tested character belongs to a particular cluster or not. The implementation is carried out completely in 'C' language. Ten sets of English alphabets (small-26, capital-26) were used to train the ANN and 5 sets of English alphabets were used to test the network. The characters were collected from different persons over duration of about 25 days. The algorithm was tested with 5 capital letters and 5 small letter sets. However, the result showed that the algorithm recognized English alphabet patterns with maximum accuracy of 92.59% and False Rejection Rate (FRR) of 0%.