Artificial neural network based character recognition using SciLab

Character recognition (CR) from an image of text is challenging research in pattern recognition and image processing. New CR systems use artificial neural network (ANN) methods embedded in commercially available software. However, with the rising cost of software, a research revolution in CR is becoming limited. In this work, a CR system is developed using open-source and free software, SciLab. It is the most desirable choice than other compensated software. CR experiments have been done using ANN. The topologies of the neural network varied to recognize ten numerals. The neural network is applied to classify the character with the online backpropagation algorithm by changing the weights for each input online. The results reveal a lower error and the system’s accuracy of 99.92%. With standard backpropagation (batch version) while varying weights after a particular batch. An error is comparatively more, and the system’s output accuracy of 99.62% for the same topology. The application of pre-processing techniques to the given images with topology optimization. The image recognition accuracy is increased by 100%. The system provided optimum results with a topology of 135–100-10. So, the online backpropagation algorithm is more accurate than the standard batch version and should be adopted. Other CR research models can be developed with the SciLab Toolboxes at no cost and with maximum system accuracy.