GridMask: An Efficient Scheme for Real Time Curved Scene Text Detection

Zhonghong Ou, Yiqun Zhang, Siyuan Yao, Meina Song

Abstract: Real-time curved scene text detection remains challenging due to various background and diverse text shapes. Existing methods, which often predict at full scale, are time-consuming, whilst low scale methods are not able to handle texts in complex scene. To resolve this problem, we propose a new quarter-scale detection scheme, named GridMask. GridMask models a 4 \(\times \) 4 pixels block efficiently and avoids post-processing. It formulates text detection as a grid classification and regression task, enabling fast execution. A comprehensive set of experiments on the curved and multi-orientation texts from four datasets, including ICDAR 2015, CTW1500, Total Text and MSRA-TD500, demonstrate that GridMask achieves state-of-the-art execution speed in scene text detection. GridMask also achieves state-of-the-art accuracy on the CTW1500 and Total Text datasets, which implies that GridMask is superior to prior studies from both perspectives. The source code and the trained model is available.