Abstract
In recent years, growing attention has been paid to recognizing text in natural scenes images. Scene Character recognition (SCR) is an important step in automatizing the process of reading text in natural scenes. In this paper, we propose a new system which deals with SCR problem. This system is based on a novel Bag of Features (BoF)-based model which use supervised dictionary learning in BoF framework using sparse neural networks models. Thus, in the learning dictionary step, we use a strategy based on neural network model combined with supervised fine-tuning. This technique provide more accuracy and more concise visual dictionary, if we compare it to the most used unsupervised dictionary learning technique like sparse coding. To evaluate our system, we test our proposed method on two English scene character benchmark datasets, i.e, Chars74K and ICDAR2003, and we propose a database of Arabic characters, called ARASTI. Experimental results show the efficiency of this framework for English and Arabic SCR recognition.