如何从图像中准确提取数据?使用 PyTesseract
How to extract data from an image accurately? Using PyTesseract
我正在尝试使用 python 从图像中准确提取文本。
这是我在这种情况下使用的图像:
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
如您所见,并非所有文字都拼写正确。有什么提高文本输出准确性的建议吗?
额外
这是我想要实现的目标的想法,与问题无关,但让您了解我在这里想要实现的目标。
我有多个产品图像文件,我将在其中与 excel sheet.
进行比较
Excel sheet 的格式如下(1 个示例数据):
Product Code: 0001
Product Desc: Californian Whole Almonds
Ingredients: Almonds: [Nuts]
Allergy Advice: True
etc...
然后我将编写一个脚本来检测图像文件中的文本,将其与 excel sheet 进行比较并分析每个部分是否匹配,给出 [= 的输出38=] 或 'False'
在将图像放入 Pytesseract 之前将图像预处理为 smooth/remove 噪音会有所帮助。也许删除 horizontal/vertical 行会改善检测
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()
我正在尝试使用 python 从图像中准确提取文本。
这是我在这种情况下使用的图像:
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
如您所见,并非所有文字都拼写正确。有什么提高文本输出准确性的建议吗?
额外
这是我想要实现的目标的想法,与问题无关,但让您了解我在这里想要实现的目标。
我有多个产品图像文件,我将在其中与 excel sheet.
进行比较Excel sheet 的格式如下(1 个示例数据):
Product Code: 0001
Product Desc: Californian Whole Almonds
Ingredients: Almonds: [Nuts]
Allergy Advice: True
etc...
然后我将编写一个脚本来检测图像文件中的文本,将其与 excel sheet 进行比较并分析每个部分是否匹配,给出 [= 的输出38=] 或 'False'
在将图像放入 Pytesseract 之前将图像预处理为 smooth/remove 噪音会有所帮助。也许删除 horizontal/vertical 行会改善检测
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()