OpenCV3的kNN算法进行OCR识别－使用Python
OpenCV3的kNN算法进行OCR识别－使用Python
http://docs.opencv.org/master/d8/d4b/tutorial_py_knn_opencv.html
Goal
In this chapter
We will use our knowledge on kNN to build a basic OCR application.
We will try with Digits and Alphabets data available that comes with OpenCV.
目标
• 要根据我们掌握的 kNN 知识创建一个基本的 OCR 程序
• 使用 OpenCV 自带的手写数字和字母数据测试我们的程序
OCR of Hand-written Digits
Our goal is to build an application which can read the handwritten digits. For this we need some train_data and test_data. OpenCV comes with an image digits.png (in the folder opencv/samples/data/) which has 5000 handwritten digits (500 for each digit). Each digit is a 20x20 image. So our first step is to split this image into 5000 different digits. For each digit, we flatten it into a single row with 400 pixels. That is our feature set, ie intensity values of all pixels. It is the simplest feature set we can create. We use first 250 samples of each digit as train_data, and next 250 samples as test_data. So let's prepare them first.
1 手写数字的 OCR
我们的目的是创建一个可以对手写数字进行识别的程序。为了达到这个目 的我们需要训练数据和测试数据。OpenCV 安装包中有一副图片(/samples/ python2/data/digits.png), 其中有 5000 个手写数字(每个数字重复 500遍)。每个数字是一个 20x20 的小图。所以第一步就是将这个图像分割成 5000个不同的数字。我们在将拆分后的每一个数字的图像重排成一行含有 400 个像 素点的新图像。这个就是我们的特征集,所有像素的灰度值。这是我们能创建 的最简单的特征集。我们使用每个数字的前 250 个样本做训练数据,剩余的250 个做测试数据。先准备一下:
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('digits.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Now we split the image to 5000 cells, each 20x20 size
cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]
# Make it into a Numpy array. It size will be (50,100,20,20)
x = np.array(cells)
# Now we prepare train_data and test_data.
train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)
test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)
# Create labels for train and test data
k = np.arange(10)
train_labels = np.repeat(k,250)[:,np.newaxis]
test_labels = train_labels.copy()
# Initiate kNN, train the data, then test it with test data for k=1
knn = cv2.KNearest()
knn.train(train,train_labels)
ret,result,neighbours,dist = knn.find_nearest(test,k=5)
# Now we check the accuracy of classification
# For that, compare the result with test_labels and check which are wrong
matches = result==test_labels
correct = np.count_nonzero(matches)
accuracy = correct*100.0/result.size
print accuracy
So our basic OCR app is ready. This particular example gave me an accuracy of 91%. One option improve accuracy is to add more data for training, especially the wrong ones. So instead of finding this training data everytime I start application, I better save it, so that next time, I directly read this data from a file and start classification. You can do it with the help of some Numpy functions like np.savetxt, np.savez, np.load etc. Please check their docs for more details.
现在最基本的 OCR 程序已经准备好了,这个示例中我们得到的准确率为91%。改善准确度的一个办法是提供更多的训练数据,尤其是判断错误的那 些数字。为了避免每次运行程序都要准备和训练分类器,我们最好把它保留, 这样在下次运行是时,只需要从文件中读取这些数据开始进行分类就可以了。Numpy 函数 np.savetxt,np.load 等可以帮助我们，具体的查看相应的文档。
1 # save the data
2 np.savez('knn_data.npz',train=train, train_labels=train_labels)
3
4 # Now load the data
5 with np.load('knn_data.npz') as data:
6 print data.files
7 train = data['train']
8 train_labels = data['train_labels']
In my system, it takes around 4.4 MB of memory. Since we are using intensity values (uint8 data) as features, it would be better to convert the data to np.uint8 first and then save it. It takes only 1.1 MB in this case. Then while loading, you can convert back into float32.
在我的系统中,占用的空间大概为 4.4M。由于我们现在使用灰度值 (unint8)作为特征,在保存之前最好先把这些数据装换成 np.uint8 格式,这样就只需要占用 1.1M 的空间。在加载数据时再转会到 float32。
OCR of English Alphabets
Next we will do the same for English alphabets, but there is a slight change in data and feature set. Here, instead of images, OpenCV comes with a data file, letter-recognition.data in opencv/samples/cpp/ folder. If you open it, you will see 20000 lines which may, on first sight, look like garbage. Actually, in each row, first column is an alphabet which is our label. Next 16 numbers following it are its different features. These features are obtained from UCI Machine Learning Repository. You can find the details of these features in this page.
There are 20000 samples available, so we take first 10000 data as training samples and remaining 10000 as test samples. We should change the alphabets to ascii characters because we can't work with alphabets directly.
英文字母的 OCR
接下来我们来做英文字母的 OCR。和上面做法一样,但是数据和特征集有 一些不同。现在 OpenCV 给出的不是图片了,而是一个数据文件(/samples/ cpp/letter-recognition.data)。如果打开它的话,你会发现它有 20000 行, 第一样看上去就像是垃圾。实际上每一行的第一列是我们的一个字母标记。接 下来的 16 个数字是它的不同特征。这些特征来源于UCI Machine Learning Repository。你可以在此页找到更多相关信息。
有 20000 个样本可以使用,我们取前 10000 个作为训练样本，剩下的10000 个作为测试样本。我们应在先把字母表转换成 asc 码,因为我们不能直接处理字母。
import cv2
import numpy as np
3 import matplotlib.pyplot as plt
4
5 # Load the data, converters convert the letter to a number
6 data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',
7 converters= {0: lambda ch: ord(ch)-ord('A')})
8
9 # split the data to two, 10000 each for train and test
10 train, test = np.vsplit(data,2)
11
12 # split trainData and testData to features and responses
13 responses, trainData = np.hsplit(train,[1])
14 labels, testData = np.hsplit(test,[1])
15
16 # Initiate the kNN, classify, measure accuracy.
17 knn = cv2.KNearest()
18 knn.train(trainData, responses)
19 ret, result, neighbours, dist = knn.find_nearest(testData, k=5)
20
21 correct = np.count_nonzero(result == labels)
22 accuracy = correct*100.0/10000
23 print accuracy
It gives me an accuracy of 93.22%. Again, if you want to increase accuracy, you can iteratively add error data in each level.
准确率达到了 93.22%。同样你可以通过增加训练样本的数量来提高准确率。