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所在平台: Udemy |
课程主页: https://www.udemy.com/course/business-card-reader-app/
课程评论：没有评论
课程名称：《智能提取文档中的文本和数据：OCR与命名实体识别》 课程概述： 欢迎参加《智能提取文档中的文本和数据：OCR与命名实体识别》课程！在本课程中，您将学习如何开发定制的命名实体识别器。课程的主要目的是从扫描文档中提取实体，例如发票、名片、运单等。出于数据隐私原因，我们主要集中在名片的处理上，但您可以使用本课程中介绍的框架处理各种金融文档。 课程内容将以两个主要的数据科学技术为基础： - 计算机视觉 - 自然语言处理 在计算机视觉模块中，我们将扫描文档、识别文本位置并从图像中提取文本。然后在自然语言处理模块中，我们将从文本中提取实体，并进行必要的文本清理和解析。 ### 主要技术： **计算机视觉模块所使用的Python库：** - OpenCV - Numpy - Pytesseract **自然语言处理模块所使用的Python库：** - Spacy - Pandas - 正则表达式 ### 课程结构： **第一阶段：项目设置** - 安装Python - 安装依赖项 **第二阶段：数据准备** - 使用Pytesseract从图像中提取文本 - 收集图像 - 提取所有图像的文本 - 清理和准备文本 **第三阶段：标记NER数据** - 使用BIO标记法进行手动标记 - B - 头部 - I - 内部 - O - 外部 **第四阶段：数据清理与预处理** - 为Spacy准备训练数据 - 将数据转换为Spacy格式 **第五阶段：训练命名实体模型** - 配置NER模型 - 训练模型 **第六阶段：使用NER模型进行实体预测** - 加载模型 - 使用Displacy渲染和展示 - 在图像上绘制边界框 - 从文本中解析实体 最终，我们将整合所有内容，创建一个文档扫描应用程序。准备好了吗？让我们开始开发这个人工智能项目吧！
Welcome to Course "Intelligently Extract Text & Data from Document with OCR NER"!!!In this course you will learn how to develop customized Named Entity Recognizer. The main idea of this course is to extract entities from the scanned documents like invoice, Business Card, Shipping Bill, Bill of Lading documents etc. However, for the sake of data privacy we restricted our views to Business Card. But you can use the framework explained to all kinds of financial documents. Below given is the curriculum we are following to develop the project.To develop this project we will use two main technologies in data science are,Computer VisionNatural Language ProcessingIn Computer Vision module, we will scan the document, identify the location of text and finally extract text from the image. Then in Natural language processing, we will extract the entitles from the text and do necessary text cleaning and parse the entities form the text.Python Libraries used in Computer Vision Module.OpenCVNumpyPytesseractPython Libraries used in Natural Language ProcessingSpacyPandasRegular ExpressionStringAs are combining two major technologies to develop the project, for the sake of easy to understand we divide the course into several stage of development.Stage -1: We will setup the project by doing the necessary installations and requirements.Install PythonInstall DependenciesStage -2: We will do data preparation. That is we will extract text from images using Pytesseract and also do necessary cleaning.Gather ImagesOverview on PytesseractExtract Text from all ImageClean and Prepare textStage -3: We will see how to label NER data using BIO tagging.Manually Labeling with BIO techniqueB - BeginningI - InsideO - OutsideStage -4: We will further clean the text and preprocess the data for to train machine learning.Prepare Training Data for SpacyConvert data into spacy formatStage -5: With the preprocess data we will train the Named Entity model.Configuring NER ModelTrain the modelStage -6: We will predict the entitles using NER and model and create data pipeline for parsing text.Load ModelRender and Serve with DisplacyDraw Bounding Box on ImageParse Entitles from TextFinally, we will put all together and create document scanner app.Are you ready!!!Let start developing the Artificial Intelligence project.
