An efficient ROI detection algorithm for Bangla text extraction and recognition from natural scene images

Abstract

This research work plays a significant role in finding information from the scene images to fulfill the demand of real life applications like detection of license plate, navigation of robot and helping the visually impaired persons. Here, a new algorithm has been proposed and applied on the scene images to extract Region of Interest (ROI). All the Bangla words are then separated from a sentence by analyzing and applying the Connected Component (CC) method along with bounding box technology. Another new algorithm has been proposed and applied to apart and bring-out Bangla characters from the Bangla words. This algorithm works by the method of vertical scanning of the images of Bangla words. Finally, the extracted characters are recognized by using the Support Vector Machine (SVM) as a classifier which works with Histogram of Oriented Gradient (HOG) features. There are 500 scene images with variations in colors, writing styles and orientations in our designed database. The proposed algorithm yields the accuracy 92.70% and 93.23% in extraction of ROI and character respectively. In the recognition of Bangla characters (digits, Basic characters, and joined characters), the average accuracy is 99.16%. The recognition accuracy of Bangla characters using Convolutional Neural Network (CNN) is also calculated and the obtained result is 83.52%.

Keywords

• HOG
• SVM
• Connected component
• Vertical projection
• Filtering
• Character recognition