EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

Content-based image analysis with applications to the multifunction printer imaging pipeline and image databases

Image understanding is one of the most important topics for various applications. Most of image understanding studies focus on content-based approach while some others also rely on meta data of images. Image understanding includes several sub-topics such as classification, segmentation, retrieval and automatic annotation etc., which are heavily studied recently. This thesis proposes several new methods and algorithms for image classification, retrieval and automatic tag generation. The proposed algorithms have been tested and verified in multiple platforms. For image classification, our proposed method can complete classification in real-time under hardware constraints of all-in-one printer and adaptively improve itself by online learning. Another image understanding engine includes both classification and image quality analysis is designed to solve the optimal compression problem of printing system. Our proposed image retrieval algorithm can be applied to either PC or mobile device to improve the hybrid learning experience. We also develop a new matrix factorization algorithm to better recover the image meta data (tag). The proposed algorithm outperforms other existing matrix factorization methods