Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Autoencoder Approach for Handwritten Arabic Digits
  Recognition   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%.

15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph