Machine Learning - XVIII. Application Example Photo OCR应用实例-照片OCR(Week10)

http://blog.csdn.net/pipisorry/article/details/44999703

机器学习Machine Learning - Andrew NG courses学习笔记

Application Example Photo OCR应用实例照片

OCR(Optical Character Recognition)光学文字辨识

Problem Description and Pipeline问题描述和管道

three reasons to centered around an application called Photo OCR:first to show you an example of how a complex machine learning system can be put together.Second, once told the concepts of a machine learning
a pipeline and how to allocate resources when you‘re trying to decide what to do next.Finally, the Photo OCR problem also tell you about a couple more interesting ideas for machine learning.One is how to apply machine learning to computer vision, and second
is the idea of artificial data synthesis.

Photo OCR pipeline

Note:some photo OCR systems do more complex things,like
spelling correction at the end.For example character segmentation and character classification system tells you that it sees word c 1 e a n i n g. Then,spelling correction system might tell you that this is probably the word ‘cleaning‘, and
your character classification algorithm had just mistaken the l for a 1.

Note:

1. A system like this is called a machine learning pipeline.In particular, here‘s a picture showing the photo OCR pipeline.

2. pipelines are common, where you can have multiple modules,each of which may be machine learning component,or sometimes it may not be a machine learning component but to have a set of modules that act one after another on some piece of data
in order to produce the output you want.

3. designing a machine learning system one of the most important decisions will often be
what exactly is the pipeline that you want to put together.In other words, given the photo OCR problem, how do you break this problem down into a sequence of different modules.And each the performance of each of the modules in your pipeline.will
often have a big impact on the final performance of your algorithm.

Sliding Windows滑窗

Getting Lots of Data and Artificial Data获取大量数据和人工数据

Ceiling Analysis:What Part of the Pipeline to Work on Next 上限分析-接下来用管道的什么部分工作

from:http://blog.csdn.net/pipisorry/article/details/44999703

时间: 2024-12-26 18:06:39

Machine Learning - XVIII. Application Example Photo OCR应用实例-照片OCR(Week10)的相关文章

Machine Learning第十一周笔记:photo OCR

博客已经迁移至Marcovaldo's blog (http://marcovaldong.github.io/) 刚刚完毕了Cousera上Machine Learning的最后一周课程.这周介绍了machine learning的一个应用:photo OCR(optimal character recognition,光学字符识别),以下将笔记整理在以下. Photo OCR Problem Description and Pipeline 最后几小节介绍机器学习的一个应用--photo O

(原创)Stanford Machine Learning (by Andrew NG) --- (week 10) Large Scale Machine Learning & Application Example

本栏目来源于Andrew NG老师讲解的Machine Learning课程,主要介绍大规模机器学习以及其应用.包括随机梯度下降法.维批量梯度下降法.梯度下降法的收敛.在线学习.map reduce以及应用实例:photo OCR.课程地址为:https://www.coursera.org/course/ml (一)大规模机器学习 从前面的课程我们知道,如果我们的系统是high variance的,那么增加样本数会改善我们的系统,假设现在我们有100万个训练样本,可想而知,如果使用梯度下降法,

斯坦福第十八课:应用实例:图片文字识别(Application Example: Photo OCR)

18.1  问题描述和流程图 18.2  滑动窗口 18.3  获取大量数据和人工数据 18.4  上限分析:哪部分管道的接下去做 18.1  问题描述和流程图 图像文字识别应用所作的事是,从一张给定的图片中识别文字.这比从一份扫描文档中 识别文字要复杂的多. 为了完成这样的工作,需要采取如下步骤: 为了完成这样的工作,需要采取如下步骤: 1. 文字侦测(Text detection)——将图片上的文字与其他环境对象分离开来 2. 字符切分(Character segmentation)——将文

机器学习---用python实现感知机算法和口袋算法(Machine Learning PLA Pocket Algorithm Application)

之前在<机器学习---感知机(Machine Learning Perceptron)>一文中介绍了感知机算法的理论知识,现在让我们来实践一下. 有两个数据文件:data1和data2,分别用于PLA和Pocket Algorithm.可在以下地址下载: 先回顾一下感知机算法: 1,初始化w 2,找出一个分类错误点 3,修正错误,假设迭代次数为t次(t=1,2,...),那么修正公式为: 4,直至没有分类错误点,返回最终的w 接下来让我们安照算法步骤,一步一步进行. 首先导入需要用到的库,其中

【MATLAB】Machine Learning (Coursera Courses Outline &amp; Schedule)

课程涉及技术: 梯度下降.线性回归.监督/非监督学习.分类/逻辑回归.正则化.神经网络.梯度检验/数值计算.模型选择/诊断.学习曲线.评估度量.SVM.K-Means聚类.PCA.Map Reduce & Data Parallelism 等- 课程涉及应用: 邮件分类.肿瘤诊断.手写识别.自动驾驶.模型优化.OCR等- Coursera machine learning course materials, including problem sets and my solutions (usi

A Gentle Guide to Machine Learning

A Gentle Guide to Machine Learning Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. Got it? We can make machines lear

A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning

A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning by Jason Brownlee on September 9, 2016 in XGBoost 0 0 0 0 Gradient boosting is one of the most powerful techniques for building predictive models. In this post you will dis

Introduction to Machine Learning

Chapter 1 Introduction 1.1 What Is Machine Learning? To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algori

Lessons learned developing a practical large scale machine learning system

原文:http://googleresearch.blogspot.jp/2010/04/lessons-learned-developing-practical.html Lessons learned developing a practical large scale machine learning system Tuesday, April 06, 2010 Posted by Simon Tong, Google Research When faced with a hard pre