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Detecting and recognizing text in natural scene images is a challenging, yetnot completely solved task. In re- cent years several new systems that try tosolve at least one of the two sub-tasks (text detection and text recognition)have been proposed. In this paper we present STN-OCR, a step towardssemi-supervised neural networks for scene text recognition, that can beoptimized end-to-end. In contrast to most existing works that consist ofmultiple deep neural networks and several pre-processing steps we propose touse a single deep neural network that learns to detect and recognize text fromnatural images in a semi-supervised way. STN-OCR is a network that integratesand jointly learns a spatial transformer network, that can learn to detect textregions in an image, and a text recognition network that takes the identifiedtext regions and recognizes their textual content. We investigate how our modelbehaves on a range of different tasks (detection and recognition of characters,and lines of text). Experimental results on public benchmark datasets show theability of our model to handle a variety of different tasks, withoutsubstantial changes in its overall network structure.

STN-OCR: A single Neural Network for Text Detection and Text Recognition