Deep Learning With Sparse Prior Application To Text Detection In The Wild

Text detection in the wild remains a very challenging task in computer vision. According to the state-of-the-art, no text detector system, robust whatever the circumstances, exists up to date. For instance, the complexity and the diversity of degradations in natural scenes make traditional text detection methods very limited and inefficient. Recent studies reveal the performance of texture-based approaches especially including deep models. Indeed, the main strengthens of these models is the availability of a learning framework coupling feature extraction and classifier. Therefore, this study focuses on developing a new texture-based approach for text detection that takes advantage of deep learning models. In particular, we investigate sparse prior in the structure of PCANet; the convolution neural network known for its simplicity and rapidity and based on a cascaded principal component analysis (PCA). The added-value of the sparse coding is the representation of each feature map via coupled dictionaries to migrate from one level-resolution to an adequate lower-resolution. The specificity of the dictionary is the use of oriented patterns well-suited for textual pattern description. The experimental study performed on the standard benchmark, ICDAR 2003, proves that the proposed method achieves very promising results.