A Detection and Verification Model Based on SSD and Encoder-Decoder Network for Scene Text Detection

Text detection in natural scene image is challenging due to text variation in size, orientation, color and complex background, contrast, and resolution. In this paper, we focus on the long text detection in complex background. In order to deal with multi-scale text variation and exploit the recognition result to enhance the detection performance, we propose a detection and verification model based on SSD and encoder-decoder network for scene text detection. First, we present a text localization neural network based on SSD, which incorporates a text detection layer into the standard SSD model and can detect horizontal texts, especially long and dense Chinese texts in natural scenes more effectively. Second, a text verification model based on the encoder-decoder network is designed to recognize and verify the initial detection results, in order to eliminate non-text areas that are falsely detected as text areas. A series of experiments have been conducted on our constructed horizontal text detection dataset, which is composed of the horizontal text images in ICDAR 2017 Competition on Reading Chinese Text in the Wild (RCTW 2017) and some scene images taken by cameras. Compared with previous approaches, experimental results show that our method has achieved the highest recall rate of 0.784 and competitive precision rate in text detection, indicating the effectiveness of our proposed method.