Python 如何准确地从图像中提取数据？使用PyTesseract

我正在尝试使用python精确地从图像中提取文本

这是我在此场景中使用的图像：

这是我的python文件：

from PIL import Image
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r'C:\Users\test\AppData\Roaming\Python\Python37\site-packages\tesseract.exe'

img=Image.open('C:/Users/test/Desktop/Everything else/work/Almonds.jpg')

text = pytesseract.image_to_string(img, lang = 'eng')


print(text)

这是我在命令提示符下运行python文件时的输出：

INGREDIENTS: Almonds: [Nuts] Allergy Advice:
For allergens, see ingredients in Bold

Nutritional Information
TYPICALVALUES Per 100g

Energy kJ 2597.0}
Energy kcal 626.0)
Fat 50.6g|

of which Saturates 3.9g

Carbohy drate 19.7g

of which Sugars 4.89|
Fibre 3.59
Protein 21.3g|

May contain traces of
other nuts, peanut,
sesame or gluten

This product may contain
pieces of shell

Store in a cool dry place
jout of direct sunlight

Net weight:



Salt 0.ig

For Best Before & Batch see pack 1 k

正如您所见，并非所有文本都拼写正确。有什么建议可以提高文本输出的准确性吗

额外的

这是我试图实现的想法，与问题无关，但给你一个我试图实现的想法

我有多个图像文件的产品，我将比较一个excel表

Excel工作表的格式如下（1个示例数据）：

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然后我将编写一个脚本，该脚本将检测图像文件中的文本，将其与excel表进行比较，并分析每个部分是否匹配，给出“真”或“假”的输出。在将图像放入PyteSeract之前，预处理图像以平滑/消除噪波可能会有所帮助。也许移除水平/垂直线将提高检测效果

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有什么想法吗？太棒了！这是从哪里来的？对于OCR来说，对图像进行预处理很重要，典型的步骤是获得二值图像，然后通过形态学变换去除噪声。删除线条的想法来自于

Product Code: 0001
Product Desc: Californian Whole Almonds
Ingredients: Almonds: [Nuts]
Allergy Advice: True
etc...

import cv2

image = cv2.imread('1.jpg',0)
thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Remove horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cv2.fillPoly(thresh, cnts, [0,0,0])

# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,45))
detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cv2.fillPoly(thresh, cnts, [0,0,0])

result = 255 - thresh

cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.waitKey()