Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has benefited from Fully Convolutional Network. Text cues contained in multi-level convolutional features are complementary for detecting scene text objects. How to explore these multi-level features is still an open problem. To tackle the above issues, we propose an Attention-based Bidirectional Long Short-Term Memory (AB-LSTM) model for scene text detection. First, word stroke regions (WSRs) and text center blocks (TCBs) are extracted by two AB-LSTM models, respectively. Then, the union of WSRs and TCBs are used to represent text objects. To verify the effectiveness of the proposed method, we perform experiments on four public benchmarks: CTW1500, Total-text, ICDAR2013, and MSRA-TD500, and compare it with existing state-of-the-art methods. Experiment results demonstrate that the proposed method can achieve competitive results, and well handle scene text objects with arbitrary shapes (i.e., curved, oriented, and horizontal forms).