提高 pytesseract 从图像中正确识别文本的能力

我正在尝试使用读取验证码pytesseract模块。大多数时候它都能提供准确的文本，但并非总是如此。

这是读取图像、操作图像以及从图像中提取文本的代码。

import cv2
import numpy as np
import pytesseract

def read_captcha():
    # opencv loads the image in BGR, convert it to RGB
    img = cv2.cvtColor(cv2.imread('captcha.png'), cv2.COLOR_BGR2RGB)

    lower_white = np.array([200, 200, 200], dtype=np.uint8)
    upper_white = np.array([255, 255, 255], dtype=np.uint8)

    mask = cv2.inRange(img, lower_white, upper_white)  # could also use threshold
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))  # "erase" the small white points in the resulting mask
    mask = cv2.bitwise_not(mask)  # invert mask

    # load background (could be an image too)
    bk = np.full(img.shape, 255, dtype=np.uint8)  # white bk

    # get masked foreground
    fg_masked = cv2.bitwise_and(img, img, mask=mask)

    # get masked background, mask must be inverted 
    mask = cv2.bitwise_not(mask)
    bk_masked = cv2.bitwise_and(bk, bk, mask=mask)

    # combine masked foreground and masked background 
    final = cv2.bitwise_or(fg_masked, bk_masked)
    mask = cv2.bitwise_not(mask)  # revert mask to original

    # resize the image
    img = cv2.resize(mask,(0,0),fx=3,fy=3)
    cv2.imwrite('ocr.png', img)

    text = pytesseract.image_to_string(cv2.imread('ocr.png'), lang='eng')

    return text

对于图像的处理，我得到了帮助堆栈溢出 https://stackoverflow.com/questions/29810128/opencv-python-set-background-colour#answer-38516242 post.

这是原始的验证码图像：

这张图片是在处理后生成的：

但是，通过使用pytesseract，我收到文本：AX#7rL.

谁能指导我如何将成功率提高到100%？

由于生成的图像中存在小孔，因此形态转换，特别是cv2.MORPH_CLOSE，要关闭孔并平滑图像，应该在这里工作

临界点 https://docs.opencv.org/3.3.0/d7/d4d/tutorial_py_thresholding.html获得二值图像（黑白）

Perform 形态学运算 https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html关闭前景中的小孔

将图像反转即可得到结果

4X#7rL

潜在的cv2.GaussianBlur()在插入超正方体之前也会有帮助

import cv2
import pytesseract

# Path for Windows
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Read in image as grayscale
image = cv2.imread('1.png',0)
# Threshold to obtain binary image
thresh = cv2.threshold(image, 220, 255, cv2.THRESH_BINARY)[1]

# Create custom kernel
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
# Perform closing (dilation followed by erosion)
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# Invert image to use for Tesseract
result = 255 - close
cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('result', result)

# Throw image into tesseract
print(pytesseract.image_to_string(result))
cv2.waitKey()