  This paper presents a new unsupervised learning approach with stacked
autoencoder (SAE) for Arabic handwritten digits categorization. Recently,
Arabic handwritten digits recognition has been an important area due to its
applications in several fields. This work is focusing on the recognition part
of handwritten Arabic digits recognition that face several challenges,
including the unlimited variation in human handwriting and the large public
databases. Arabic digits contains ten numbers that were descended from the
Indian digits system. Stacked autoencoder (SAE) tested and trained the MADBase
database (Arabic handwritten digits images) that contain 10000 testing images
and 60000 training images. We show that the use of SAE leads to significant
improvements across different machine-learning classification algorithms. SAE
is giving an average accuracy of 98.5%.
