Deep Learning based Isolated Arabic Scene Character Recognition

TL;DR

This paper presents a deep learning approach using CNNs for recognizing isolated Arabic characters in natural scene images, addressing variations like slanting and skewing to improve recognition accuracy.

Contribution

The work introduces a CNN-based method with multiple orientations and filter sizes for robust Arabic scene character recognition, which is a novel application in this context.

Findings

Encouraging recognition results on Arabic scene images

Effective handling of character variations like slanting and skewing

Use of multiple orientations improves robustness

Abstract

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.