<p>我根据你雕刻的文本样本计算出了一个肯定的结果。在</p>
<pre><code>import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def show(img):
plt.imshow(img, cmap="gray")
plt.show()
# load the input image
img = cv2.imread('./imagesStackoverflow/engraved_text.jpg',0);
show(img)
ret, mask = cv2.threshold(img, 60, 120, cv2.THRESH_BINARY) # turn 60, 120 for the best OCR results
kernel = np.ones((5,3),np.uint8)
mask = cv2.erode(mask,kernel,iterations = 1)
show(mask)
# I used a version of OpenCV with Tesseract, you may use your pytesseract and set the modes as:
# OCR Enginer Mode (OEM) = 3 (defualt = 3)
# Page Segmentation mode (PSmode) = 11 (defualt = 3)
tesser = cv2.text.OCRTesseract_create('C:/Program Files/Tesseract 4.0.0/tessdata/','eng','0123456789',11,3)
retval = tesser.run(mask, 0) # return string type
print 'OCR:' + retval
</code></pre>
<p>处理后的图像和OCR输出:</p>
<p><a href="https://i.stack.imgur.com/VeGrq.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VeGrq.png" alt="enter image description here"/></a></p>
<p>如果你能用更多的样本图像反馈你的测试结果,那就太好了。在</p>
<p><a href="/questions/tagged/opencv" class="post-tag" title="show questions tagged 'opencv'" rel="tag">opencv</a><a href="/questions/tagged/python" class="post-tag" title="show questions tagged 'python'" rel="tag">python</a><a href="/questions/tagged/tesseract" class="post-tag" title="show questions tagged 'tesseract'" rel="tag">tesseract</a><a href="/questions/tagged/ocr" class="post-tag" title="show questions tagged 'ocr'" rel="tag">ocr</a></p>