Python/OpenCV - 基于机器学习的OCR(图像到文本)

Question

我正在尝试通过Python 2.7接口使用OpenCV来实现基于机器学习的OCR应用程序来解析图像文件中的文本.我正在使用本教程(为方便起见,我已重新发布以下代码).我是机器学习的新手,对OpenCV来说相对较新.

手写数字的OCR:

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

# save the data
np.savez('knn_data.npz',train=train, train_labels=train_labels)

# Now load the data
with np.load('knn_data.npz') as data:
    print data.files
    train = data['train']
    train_labels = data['train_labels']

英文字母OCR:

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the data, converters convert the letter to a number
data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',
                    converters= {0: lambda ch: ord(ch)-ord('A')})

# split the data to two, 10000 each for train and test
train, test = np.vsplit(data,2)

# split trainData and testData to features and responses
responses, trainData = np.hsplit(train,[1])
labels, testData = np.hsplit(test,[1])

# Initiate the kNN, classify, measure accuracy.
knn = cv2.KNearest()
knn.train(trainData, responses)
ret, result, neighbours, dist = knn.find_nearest(testData, k=5)

correct = np.count_nonzero(result == labels)
accuracy = correct*100.0/10000
print accuracy

第二个代码片段(用于英文字母)以.data下列格式从文件中获取输入:

T,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8
I,5,12,3,7,2,10,5,5,4,13,3,9,2,8,4,10
D,4,11,6,8,6,10,6,2,6,10,3,7,3,7,3,9
N,7,11,6,6,3,5,9,4,6,4,4,10,6,10,2,8
G,2,1,3,1,1,8,6,6,6,6,5,9,1,7,5,10
S,4,11,5,8,3,8,8,6,9,5,6,6,0,8,9,7
B,4,2,5,4,4,8,7,6,6,7,6,6,2,8,7,10

......大约有20,000行.数据描述了字符的轮廓.

我已经基本掌握了它是如何工作的,但我对如何使用它来实际对图像执行OCR感到困惑.如何使用此代码编写一个函数,该函数将cv2图像作为参数并返回表示已识别文本的字符串？

Answer 1

一般来说,机器学习的工作方式如下:首先,您必须训练您的程序,以了解您的问题领域.然后你开始提问.

因此,如果您正在创建OCR,第一步是教您的程序A字母是什么样的,B等等.

您可以使用OpenCV从噪声中清除图像,并识别可能是字母的像素组并隔离它们.

然后你将这些信件提供给你的OCR程序.在训练模式下,您将提供图像并解释图像代表的字母.在询问模式下,您将提供图像并询问它是哪个字母.训练越好,你的答案就越精确(程序可能会错误地写出来,总有可能出现这种情况).