Feature-Level Fusion using Convolutional Neural Network for Multi-Language Synthetic Character Recognition in Natual Images

In this paper, a new Convolutional Neural Network (CNN) architecture is proposed for synthetic Urdu and English character recognition in natural scene images. The features are extracted using three separate sub-models of the CNN which are then fused in one feature vector. The network is purely trained on the synthetic character images of English and Urdu texts in natural images. For English text, the Chars74k-Font dataset is used and for Urdu text, the synthetic dataset is created by automatically cropping the image patches from four background image datasets and then putting characters at random positions within the image patch. The network is evaluated on a combined synthetic dataset of English and Urdu characters and the separate synthetic characters of Urdu and English datasets. The experimental results show that the network performs well on synthetic datasets.