Arbitrary-shaped text detection with adaptive convolution and path enhancement pyramid network

Abstract

Recently, scene text detection has become an active research field, which is an essential component of scene text reading. Especially, segmentation-based methods are commonly used, since the segmentation results can describe text of arbitrary shape. However, curve texts have a diversity of shapes, scales and orientations, which are difficult to locate, so the detector requires to adjust the local receptive fields size adaptively, which can aggregate multi-scale spatial information to accurately locate the curve text instance. Moreover, the low-level features are critical for localizing large text instances. When using Feature Pyramid Network (FPN) for multi-scale feature fusion, it will prevent the flow of accurate localization signals due to the long path from low-level to top-level. In order to solve these two problems, this paper proposes an Adaptive Convolution and Path Enhancement Pyramid Network (ACPEPNet), which can more accurately locate the text instances with arbitrary shapes. Firstly, an Adaptive Convolution Unit is introduced to improve the ability of backbone to aggregate multi-scale spatial information at the same stage. Specially, this unit is a lightweight component and without the cost of computations, based on this component we present a backbone network for text features extraction. Secondly, the original FPN structure is redesigned to build a short path from the low-level to top-level, in this way, we modify the path from one-way flow to two-way flow and add original features to the final stage of information fusion. Experiments on CTW1500, Total-Text, ICDAR 2015 and MSRA-TD500 validate the robustness of the proposed method. When there is no bells and whistles, this method achieves an F-measure of 80.8% without external training data on CTW1500.