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():
leftis the distance from the upper-left corner of the bounding
box, to the left border of the image.topis the distance from the upper-left corner of the bounding box,
to the top border of the image.widthandheightare the width and height of the bounding box.confis the model’s confidence for the prediction for the word within that bounding box. Ifconfis -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.