Text detection in low resolution scene images using convolutional neural network

Text detection on scene images has increasingly gained a lot of interests, especially due to the increase of wearable devices. However, the devices often acquire low resolution images, thus making it difficult to detect text due to noise. Notable method for detection in low resolution images generally utilizes many features which are cleverly integrated and cascaded classifiers to form better discriminative system. Those methods however require a lot of hand-crafted features and manually tuned, which are difficult to achieve in practice. In this paper, we show that the notable cascaded method is equivalent to a Convolutional Neural Network (CNN) framework to deal with text detection in low resolution scene images. The CNN framework however has interesting mutual interaction between layers from which the parameters are jointly learned without requiring manual design, thus its parameters can be better optimized from training data. Experiment results show the efficiency of the method for detecting text in low resolution scene images.