论文标题
实时文本检测和识别
Real-Time Text Detection and Recognition
论文作者
论文摘要
Inretentys，卷积alneuralnet-work（CNN）是一个非常受欢迎的话题，因为它是一种强大的和智慧的技术，可以在各个领域应用。Yolo是一种使用算法来实时文本检测任务的技术。但是，诸如光度法和几何变形之类的问题可能会影响Systemyolo的准确性并导致系统故障。因此，可以改进可以使系统更好地工作。在这篇论文中，我们将提出解决方案 - 快速准确的实时文本方向和识别系统的潜在解决方案。本文涵盖了三个主要领域的真实timetext检测和识别的主题：1。视频和图像预处理，2。文本检测，3。文本识别。 ASA成熟的技术，有许多现有方法可以肯定地改善解决方案。我们将在文献审查课程中介绍一些现有方法。在此期间，我们正在提出工业实力，高临界性，实时文本检测和识别工具。
Inrecentyears,ConvolutionalNeuralNet-work(CNN) is quite a popular topic, as it is a powerful andintelligent technique that can be applied in various fields.The YOLO is a technique that uses the algorithms for real-time text detection tasks. However, issues like, photometricdistortion and geometric distortion, could affect the systemYOLO accuracy and cause system failure. Therefore, thereare improvements that can make the system work better. Inthis paper, we are going to present our solution - a potentialsolution of a fast and accurate real-time text direction andrecognition system. The paper covers the topic of Real-TimeText detection and recognition in three major areas: 1. videoand image preprocess, 2. Text detection, 3. Text recognition. Asa mature technique, there are many existing methods that canpotentially improve the solution. We will go through some ofthose existing methods in the literature review session. In thisway, we are presenting an industrial strength, high-accuracy,Real-Time Text Detection and recognition tool.
