Pytesseract 无法识别文本

Question

我正在尝试使用 Pytesseract 从下图中读取数字：

Low Resolution Image

不幸的是，即使在使用灰度、阈值、噪声检测或精确边缘检测之后，程序也没有返回任何解决方案。当使用配置仅将数字和 $/ 列入白名单时，程序甚至停止检测高分辨率图像。 (here)

代码如下：

class NumberAnalyser:

    # boilerplate code to pre-process image
    # get grayscale image
    def get_grayscale(self, image):
        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # noise removal
    def remove_noise(self, image):
        return cv2.medianBlur(image, 5)

    # thresholding
    def thresholding(self, image):
        gray = self.get_grayscale(image)
        (T, threshInv) = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
        # visualize only the masked regions in the image
        masked = cv2.bitwise_not(gray, gray, mask=threshInv)
        ret, thresh1 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
        ret, thresh2 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
        ret, thresh3 = cv2.threshold(gray, 127, 255, cv2.THRESH_TRUNC)
        ret, thresh4 = cv2.threshold(gray, 127, 255, cv2.THRESH_TOZERO)
        ret, thresh5 = cv2.threshold(gray, 127, 255, cv2.THRESH_TOZERO_INV)
        return thresh4

    # dilation
    def dilate(self, image):
        kernel = np.ones((5, 5), np.uint8)
        return cv2.dilate(image, kernel, iterations=1)

    # erosion
    def erode(self, image):
        kernel = np.ones((5, 5), np.uint8)
        return cv2.erode(image, kernel, iterations=1)

    # opening - erosion followed by dilation
    def opening(self, image):
        kernel = np.ones((5, 5), np.uint8)
        return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)

    # canny edge detection
    def canny(self, image):
        return cv2.Canny(image, 100, 200)

    # skew correction
    def deskew(self, image):
        coords = np.column_stack(np.where(image > 0))
        angle = cv2.minAreaRect(coords)[-1]
        if angle < -45:
            angle = -(90 + angle)
        else:
            angle = -angle
            (h, w) = image.shape[:2]
            center = (w // 2, h // 2)
            M = cv2.getRotationMatrix2D(center, angle, 1.0)
            rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
            return rotated

    # template matching
    def match_template(self, image, template):
        return cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)

    def numbers(self, img_path):

        reader = cv2.imread(img_path)
        # reader = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_RGB2BGR)'

        gray = self.get_grayscale(reader)
        thresh = self.thresholding(reader)
        opening = self.opening(reader)
        canny = self.canny(reader)
        noiseless = self.remove_noise(reader)

        # cv2.imshow('canny', canny)
        # cv2.waitKey(0)
        # cv2.imshow('gray', gray)
        # cv2.waitKey(0)
        cv2.imshow('threshold', thresh)
        cv2.waitKey(0)
        # cv2.imshow('opening', opening)
        # cv2.waitKey(0)
        # cv2.imshow('noise removal', noiseless)
        # cv2.waitKey(0)
        # cv2.imshow('og', reader)
        # cv2.waitKey(0)

        print('yes')
        print(pt.image_to_string(thresh, config='--psm 11, -c tessedit_char_whitelist=$,0123456789'))

--psm 11 配置添加/删除不会改变任何东西。

任何帮助将不胜感激！

Answer 1

您连续应用多个简单阈值，但您还应该使用其他类型的阈值（例如 adaptive 和 inRange）对其进行测试。

例如，如果您在给定示例中使用inRange thresholding：

高分辨率图像的结果将是：

0.38 版本的输出：

20000
4.000
100

低分辨率图像的结果将是：

0.38 版本的输出：

44.900
16.000
34

很遗憾，只能正确识别中间的数字。如果您设置范围值，生成的图像可能会给出更好的结果。

更多阅读：Improving the quality of the output Tesseract documentation

代码：

import cv2
import pytesseract
from numpy import array

img = cv2.imread("eO1XG.png")  # Load the images: high-res: l9Zbt.png, low-res: eO1XG.png
img = cv2.cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
msk = cv2.inRange(img, array([94, 0, 196]), array([179, 84, 255]))  # for low resolution
# msk = cv2.inRange(img, array([0, 0, 0]), array([179, 26, 255]))  # for high resolution
krn = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
dlt = cv2.dilate(msk, krn, iterations=1)
thr = 255 - cv2.bitwise_and(dlt, msk)
txt = pytesseract.image_to_string(thr, config='--psm 6 digits')
print(txt)
cv2.imshow("", thr)
cv2.waitKey(0)