Comparison of autoencoder and Principal Component Analysis followed by neural network for e-learning using handwritten recognition
Jasem Almotiri, Khaled M. Elleithy, Abdelrahman Elleithy · 2017
This paper presents two different implementations for recognition of handwritten numerals using a high performance autoencoder and Principal Component Analysis (PCA) by making use of neural networks. Different from other approaches, the non-linear mapping capability of neural networks is used extensively here. The implementation involves the deployment of a neural network, and the use of an auto encoder and PCA while carrying out the compression and classification of data. The performance of the system was analyzed, and an accuracy of 97.2% for Principal Component Analysis, and 98.1% accuracy for the autoencoder, was recorded in detection of numerals written by school children.