Publicado en 3C Tecnología. Special Issue – May 2019
The biggest challenge for natural language processing systems is to accurately identify and classify the hand–written characters. Accurate Handwritten Character recognition is a challenging task for humans too as the style, size and other handwriting parameters may vary from human to human. Though a relatively straightforward machine vision task but improved accuracy as compared to the existing implementations is still desirable. This manuscript aims to propose a novel neural network based framework for handwritten character recognition. The proposed neural network based framework, transforms the raw data set to a umPy array to achieve image flattening and feeds the same into a pixel vector before feeding it into the network. In the neural network, the activation function is applied to transfer the resultant value to the hidden layer where it is further minimized through the use of minimized mean square and back propagation algorithms before applying a stochastic gradient on the resultant mini–batches. After a detailed study, the optimal algorithm for effective handwritten character recognition was proposed. Initially, the framework has been simulated only on digits through 50,000 training data samples, 10,000 validation data set and 10,000 test data set, the accuracy of 96.08. This manuscript aims to give the reader an insight into how the proposed neural network based framework has been applied for handwritten digit recognition. It highlights the successful applications of the same while laying down the directions for the enhancements possible.
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Natural Language Processing, Handwritten Character Recognition, Neural Networks, Machine Vision; Digit Recognition.