Detecting arbitrarily oriented text in natural images is still a challenging and unsolved problem in multimedia. In this work, we propose an efficient and accurate scene text detector. The detector first detects key points which are carefully designed and semantically meaningful. Then the key points are learnt to be associated together to form a hexagon for each text instance. Starting from a key point, the detector then predicts curves alongside the border of the text region. Simple heuristic post-processing followed by the predicted curve resulting in more accurate text region prediction. The predicted key points are used as anchors to correct errors from the search process. The proposed method is efficient since it is a single stage key point detection method with simple post-processing. It is also effective and shows state-of-the-art or comparable performance on several benchmark datasets.