如何识别检查叶数据

Question

再会。我正在尝试识别下面的印刷和手写文本，请检查叶子

，这是预处理后的图像，在下面使用，代码

import cv2 
import pytesseract
import numpy as np

img = cv2.imread('Images/cheque_leaf.jpg')

# Rescaling the image (it's recommended if you’re working with images that have a DPI of less than 300 dpi)
img = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
h, w = img.shape[:2]

# By default OpenCV stores images in BGR format and since pytesseract assumes RGB format,
# we need to convert from BGR to RGB format/mode:
# it to reduce noise
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]  # perform OTSU threhold
thresh = cv2.rectangle(thresh, (0, 0), (w, h), (0, 0, 0), 2) # draw a rectangle around regions of interest in an image

# Dilates an image by using a specific structuring element.
# enrich the charecters(to large)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))

# The function erodes the source image using the specified structuring element that determines 
# the shape of a pixel neighborhood over which the minimum is taken
erode = cv2.erode(thresh, kernel, iterations = 1)

# To extract the text
custom_config = r'--oem 3 --psm 6'
pytesseract.image_to_string(thresh, config=custom_config)

，现在使用pytesseract。 image_to_string（）方法将图像转换为文本。在这里，我正在获得无备用的输出。在上面的图像中，我想确定日期，分支机构，数字和措辞的金额以及数字签名名称，然后是帐号。

通过提取上述确切数据来解决上述问题的任何OCR技术。提前致谢

Answer 1

以下只是几种方法之一。

我建议使用沙文纱阈值技术。使用特定公式这里提到的。它涉及计算某个窗口中像素值的平均值和标准偏差。

此功能可在spimage库中可用（也称为scikit-image）

以下是​​给定图像的工作示例：

from skimage.filters import threshold_sauvola
img = cv2.imread('cheque.jpg', cv2.IMREAD_GRAYSCALE)

# choosing a window size of 13 (feel free to change it and visualize)
thresh_sauvola = threshold_sauvola(img, window_size=13)
binary_sauvola = img > thresh_sauvola

# converting resulting Boolean array to unsigned integer array of 8-bit (0 - 255) 
binary_sauvola_int = binary_sauvola.astype(np.uint8)
result = cv2.normalize(binary_sauvola_int, dst=None, alpha=0, beta=255,norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

结果：

注意： 此结果只是尝试其他图像处理技术以获得所需结果的启动板。