HMM如何用于手写识别? [英] How can HMMs be used for handwriting recognition?

问题描述

该问题与传统的手写识别有所不同.我有一个成千上万的数据集.对于一个绘制的字符，我有几个连续的(x, y)坐标，按下了笔.因此，这是一个连续的(时间)问题.

The problem is a bit different than traditional handwriting recognition. I have a dataset that are thousands of the following. For one drawn character, I have several sequential (x, y) coordinates where the pen was pressed down. So, this is a sequential (temporal) problem.

我希望能够根据此数据对手写字符进行分类，并且希望实现HMM来进行学习.但是，这是正确的方法吗?如何使用它们来做到这一点?

I want to be able to classify handwritten characters based on this data, and would love to implement HMMs for learning purposes. But, is this the right approach? How can they be used to do this?

推荐答案

我认为HMM可以用于@jens提到的两个问题.我也在从事在线手写，很多文章都使用HMM.最简单的方法是这样的:

I think HMM can be used in both problems mentioned by @jens. I'm working on online handwriting too, and HMM is used in many articles. The simplest approach is like this:

1. 选择一个功能.
2. 如果所选功能是连续的，请将其转换为离散的.
3. 选择HMM参数:拓扑和状态数.
4. 使用HMM训练角色模型.每个班级一个模型.
5. 使用测试集进行测试.

每个项目:

1. 最简单的特征是连接连续的矢量角度 点.您可以使用更复杂的功能，例如矢量角度 由道格拉斯& Peucker算法.
2. 离散化的最简单方法是使用Freeman代码，但是 也可以使用k-means和GMM之类的聚类算法.
3. HMM拓扑:遍历，左右，巴基斯和线性.状态数 可以通过试用获得错误. HMM参数可以是可变的 每个模型.观察数由离散化确定. 观察样本的长度可以可变.
4. 我推荐Kevin Murphy HMM工具箱.
5. 祝你好运.

1. the simplest feature is angle of vector which connects consecutive points. You can use more complicated features like angles of vectors obtained by Douglas & Peucker algorithm.
2. the simplest way for discretization is using Freeman codes, but clustering algorithms like k-means and GMM can be used too.
3. HMM topologies: Ergodic, Left-Right, Bakis and Linear. # of states can be obtained by trial & error. HMM parameters can be variable for each model. # of observations is determined by discretization. observation samples can be have variable length.
4. I recommend Kevin Murphy HMM toolbox.
5. Good luck.

这篇关于HMM如何用于手写识别?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！