Multilingual Scene Character Recognition System using Sparse Auto-Encoder for Efficient Local Features Representation in Bag of Features
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The recognition of texts existing in camera-captured images has become an important issue for a great deal of research during the past few decades. This give birth to Scene Character Recognition (SCR) which is an important step in scene text recognition pipeline. In this paper, we extended the Bag of Features (BoF)-based model using sparse stacked auto-encoder technique for representing local features for accurate SCR of different languages. In the features coding step, a Sparse Auto-encoder (SAE)-based strategy was applied to enhance the representative and discriminative abilities of image features. This technique provides more efficient features representation and therefore a better recognition accuracy, compared to other feature coding techniques. This coding step was followed by a Spatial Pyramid Matching (SPM) and max-pooling to keep the spatial information and form the global image signature. Our system was evaluated extensively on six scene character datasets of five different languages. The experimental results proved the efficiency of our system for a multilingual SCR.
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