Use pytesseract.image_to_data()
import pytesseract
from pytesseract import Output
import cv2
img = cv2.imread('image.jpg')
d = pytesseract.image_to_data(img, output_type=Output.DICT)
n_boxes = len(d['level'])
for i in range(n_boxes):
(x, y, w, h) = (d['left'][i], d['top'][i], d['width'][i], d['height'][i])
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('img', img)
cv2.waitKey(0)
Among the data returned by pytesseract.image_to_data()
:
left
is the distance from the upper-left corner of the bounding
box, to the left border of the image.
top
is the distance from the upper-left corner of the bounding box,
to the top border of the image.
width
and height
are the width and height of the bounding box.
conf
is the model's confidence for the prediction for the word within that bounding box. If conf
is -1, that means that the corresponding bounding box contains a block of text, rather than just a single word.
The bounding boxes returned by pytesseract.image_to_boxes()
enclose letters so I believe pytesseract.image_to_data()
is what you're looking for.
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