A New Parallel Detection-Recognition Approach for End-to-End Scene Text Extraction

In this work, we present a new conceptually simple and flexible network for accurate scene text extraction, which handle text detection and text recognition concurrently. Apart from solving feature sharing by designing a unified network, we implement end-to-end training of the whole system by a set of new optimization strategies. More importantly, this method highlights its novel parallel detection-recognition structure, which constructs a loose connection between both detection and recognition. This loose connection is embodied in the definition of overall loss and derivative back propagation for the model parameter updating, which automatically balances the contribution of the two branches to the system performance. It is different from the existing end-to-end methods where two subtasks are connected serially and thus yielding heavy dependence of the predecessor text detection task on the follow-up text recognition task and sensitivity of recognition to detection noise. In addition, a simple Mask-Rectifier mechanism is applied to easily adapt our system to incidental text recognition with arbitrary orientation and shape. Experiment results on Incidental Scene Text ICDAR2015 dataset surpass the current state-of-the-art FOTS method, as suggests the effectiveness of the proposed approach.