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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ocr-optical-character-recognition-in-python/
课程评论：没有评论
课程名称：Python中的光学字符识别（OCR） 课程概述：光学字符识别（OCR）是计算机视觉领域中的一个子领域，致力于将图像转换为文本。OCR的目的在于将包含输入、手写或打印文本的图像转化为机器能够理解的字符。通过OCR，可以将扫描或拍摄的文档转换为可以在任何工具中编辑的文本，例如微软Word。其常见应用包括自动表单读取，用户可以发送信用卡或驾照的照片，系统可以自动识别所有数据而无需手动输入。此外，自驾车技术也可利用OCR读取交通标志，而停车场可以通过读取汽车的牌照来确保车辆的进出。 在本课程中，您将实践学习如何使用OCR库识别图像和视频中的文本，所有代码将通过Python编程语言逐步实现。课程将在Google Colab上进行，您无需担心本地安装库，因为所有开发将在使用Google GPU的在线环境中完成！您还将学习如何使用深度学习和卷积神经网络从零开始构建自己的OCR。 课程的主要内容包括： - 使用Tesseract、EasyOCR和EAST在图像和视频中识别文本 - 使用正则表达式在图像中搜索特定术语 - 提升图像质量的技术，如阈值处理、颜色反转、灰度处理、缩放、去噪、形态学操作与透视变换 - 使用EAST架构和EasyOCR库在自然场景中获取更好的性能 - 使用TensorFlow和现代深度学习技术（如卷积神经网络）从头训练OCR - 在OCR提取的文本中应用自然语言处理技术（词云和命名实体识别） - 车牌识别 这些都是课程的主要主题之一！课程结束时，您将掌握创建自己文本识别项目所需的所有知识，运用OCR技术实现各种应用。
Within the area of Computer Vision is the sub-area of Optical Character Recognition (OCR), which aims to transform images into texts. OCR can be described as converting images containing typed, handwritten or printed text into characters that a machine can understand. It is possible to convert scanned or photographed documents into texts that can be edited in any tool, such as the Microsoft Word. A common application is automatic form reading, in which you can send a photo of your credit card or your driver's license, and the system can read all your data without the need to type them manually. A self-driving car can use OCR to read traffic signs and a parking lot can guarantee access by reading the license plate of the cars!To take you to this area, in this course you will learn in practice how to use OCR libraries to recognize text in images and videos, all the code implemented step by step using the Python programming language! We are going to use Google Colab, so you do not have to worry about installing libraries on your machine, as everything will be developed online using Google's GPUs! You will also learn how to build your own OCR from scratch using Deep Learning and Convolutional Neural Networks! Below you can check the main topics of the course:Recognition of texts in images and videos using Tesseract, EasyOCR and EASTSearch for specific terms in images using regular expressionsTechniques for improving image quality, such as: thresholding, color inversion, grayscale, resizing, noise removal, morphological operations and perspective transformationEAST architecture and EasyOCR library for better performance in natural scenesTraining an OCR from scratch using TensorFlow and modern Deep Learning techniques, such as Convolutional Neural NetworksApplication of natural language processing techniques in the texts extracted by OCR (word cloud and named entity recognition)License plate readingThese are just some of the main topics! By the end of the course, you will know everything you need to create your own text recognition projects using OCR!
