Text Siamese Network for Video Textual Keyframe Detection In this paper, we propose a novel approach of video text keyframe detection, to achieve the goal of representing the video with textual keyframes and reducing the waste of resources in video review. Different from the works of video summarization which mainly focus on the variation of the scenes in videos, we pay attention to the variances of the text between sequential frames. For the above purpose, Text Siamese Network (TSN) is developed to automatically detect the keyframes which contain text in videos. Specifically, the TSN is composed of two branches, text similarity measurement and text identification. The first branch is utilized to evaluate the similarity between consecutive frames. Furthermore, an attention block is used to select the informative features to identify whether a frame contains text or not in the second branch. Additionally, a new dataset called VTKD2019 is proposed for video text keyframe detection. VTKD2019 contains 571 videos and is spit into three levels (easy, medium and hard) for evaluation. The experimental results on the VTKD2019 and ICDAR2015 demonstrate the effectiveness of our method.

An EndtoEnd Video Text Detector with Online Tracking Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods attempt to enhance the performance of video text detection by cooperating with video text tracking, but treat these two tasks separately. In this work, we propose an endtoend video text detection model with online tracking to address these two challenges. Specifically, in the detection branch, we adopt ConvLSTM to capture spatial structure information and motion memory. In the tracking branch, we convert the tracking problem to text instance association, and an appearancegeometry descriptor with memory mechanism is proposed to generate robust representation of text instances. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Experiments on existing video text benchmarks including ICDAR2013 Video, Minetto and YVT demonstrate that the proposed method significantly outperforms stateoftheart methods. Our method improves Fscore by about 2% on all datasets and it can run realtime with 24.36 fps on TITAN Xp.