Name
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 × 3 and 5 × 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ASAR.2017.8067758 | 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) |
Keywords | DocType | Volume |
Deep Learning,Convolutional,Scene Text | Conference | abs/1704.06821 |
ISBN | Citations | PageRank |
978-1-5090-6629-2 | 3 | 0.38 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saad Bin Ahmed | 1 | 19 | 3.07 |
| Saeeda Naz | 2 | 147 | 14.25 |
| Muhammad Imran Razzak | 3 | 221 | 33.86 |
| Rubiyah Yousaf | 4 | 11 | 0.99 |