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Title: Application of text detection in nature scene
Authors: Zhu, Yuncong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: The technology for obtaining information from big data has broad application prospects. Among them, the information contained in the text is direct and efficient, furthermore the digitizing of the text information is of great significance for improving capabilities of both the multimedia retrieval and scene understanding. Recently, the identification of document text is mature. But the recognition of text in images, especially in natural scene images, which is important and challenging, is still in the stage of research and exploration. Because when dealing with natural scene images, even if using deep neural network models, we still have problems. The reason is that the overall performance of the network depends on the interaction of multiple stages. So that we want to propose a simple, effective and fast network for text detection. This dissertation explores the transition from object recognition to text recognition and the development of natural scene text recognition. A brief review of the principles, structure and design of the deep learning method of text detection, namely the convolutional neural network method. Then we present a simple and useful network that produces fast and accurate text detection in natural scene images. The network can directly predict words or lines of text in any direction and quadrilateral shape in the image, eliminating unnecessary steps. From the experimental results, we can see the accuracy and efficiency of the algorithm, and then propose an improved method in the dissertation. Using the improved method, the calculation speed is improved, the work efficiency is improved, while the accuracy of the text detection result is maintained. By analyzing the experimental results, we confirmed that this is a useful algorithm. Finally, the conclusion and recommendations about the further research will be given.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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