Handwriting Digit Recognition using Local Binary Pattern Variance and Artificial neural network Classification(ANN)"

In base paper present about handwriting digit recognition system using Local Binary Pattern as feature extraction method and K-Nearest Neighbor as classification algorithm. So I would like to do the same work by using Artificial neural networks (ANN) as machine learning classification algorithm. In base paper the testing result on this system shows that the Local Binary Pattern Variance method can recognize handwriting digit character on MNIST dataset with accuracy level 89,81% using the best parameter value radius=4,256 and 64-bin histogram, 9 region division on the image, and 10 nearest neighbor on K-NN algorithm. When tested using the data from C1 form , the system accuration value is 70,91%. Aim of my project is to increase the accuracy of predictions using ANN.

PFA for base paper.

Habilidades: Algoritmos, Java, Machine Learning (ML), Mathlab y Mathematica, Python

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Información del empleador:
( 0 comentarios ) Bengaluru, India

Nº del proyecto: #18157145

23 freelancers están ofertando el promedio de ₹27354 para este trabajo


Hi, I have already worked on similar project in which I identified persons based on hishandwriting. I have also worked on digits recognition. You can also check our video of signature verification here [login to view URL] Más

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