Abstract
Handwritten Digit Recognition (HDR) is a challenging research area in the field of Optical Character Recognition. In the branch of computer vision, HDR gaining a huge demand and become the popular machine learning problem. Recognition methods based on Artificial Neural Networks have been studied for several years in order to achieve performances close to those observed in humans. Artificial Neural Networks are proved their effectiveness in the areas of image processing. The existing methods in current image recognition use as inputs all the pixels of the image. The purpose of this work is to minimize the number of pixels by using as input the data extracted and calculated from the initial image. The approach consists of transforming the image of the digit in the binary format then encode each column by value i.e. passage of the binary representation in decimal value. This technique called column decimal coding. The architecture of Artificial Neural Network used in this research is based on a multilayer perceptron neural network in order to recognize and predict the handwritten digit from 0 to 9. A dataset of 6000 samples was obtained from the MNIST database. For better training and testing dataset, we have used the backpropagation as a learning algorithm. The proposed approach presented in this work gives the best accuracy in the majority of the test.
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Datsi, T., Aznag, K., El Oirrak, A. (2022). A Novel Approach for Handwritten Digit Recognition Using Multilayer Perceptron Neural Network. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1418. Springer, Cham. https://doi.org/10.1007/978-3-030-90639-9_19
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