  The technological advancement and sophistication in cameras and gadgets
prompt researchers to have focus on image analysis and text understanding. The
deep learning techniques demonstrated well to assess the potential for
classifying text from natural scene images as reported in recent years. There
are variety of deep learning approaches that prospects the detection and
recognition of text, effectively from images. In this work, we presented Arabic
scene text recognition using Convolutional Neural Networks (ConvNets) as a deep
learning classifier. As the scene text data is slanted and skewed, thus to deal
with maximum variations, we employ five orientations with respect to single
occurrence of a character. The training is formulated by keeping filter size 3
x 3 and 5 x 5 with stride value as 1 and 2. During text classification phase,
we trained network with distinct learning rates. Our approach reported
encouraging results on recognition of Arabic characters from segmented Arabic
scene images.
