I am using python-tesseract to extract words from an image. This is a python wrapper for tesseract which is an OCR code.
I am using the following code for getting the words:
import tesseract
api = tesseract.TessBaseAPI()
api.Init(".","eng",tesseract.OEM_DEFAULT)
api.SetVariable("tessedit_char_whitelist", "0123456789abcdefghijklmnopqrstuvwxyz")
api.SetPageSegMode(tesseract.PSM_AUTO)
mImgFile = "test.jpg"
mBuffer=open(mImgFile,"rb").read()
result = tesseract.ProcessPagesBuffer(mBuffer,len(mBuffer),api)
print "result(ProcessPagesBuffer)=",result
This returns only the words and not their location/size/orientation (or in other words a bounding box containing them) in the image. I was wondering if there is any way to get that as well
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.