How to detect subscript numbers in an image using OCR?

I am using tesseract for OCR, via the pytesseract bindings. Unfortunately, I encounter difficulties when trying to extract text including subscript-style numbers - the subscript number is interpreted as a letter instead.

For example, in the basic image:

I want to extract the text as "CH3", i.e. I am not concerned about knowing that the number 3 was a subscript in the image.

My attempt at this using tesseract is:

import cv2
import pytesseract

img = cv2.imread('test.jpeg')

# Note that I have reduced the region of interest to the known 
# text portion of the image
text = pytesseract.image_to_string(
    img[200:300, 200:320], config='-l eng --oem 1 --psm 13'
)
print(text)

Unfortunately, this will incorrectly output

'CHs'

It's also possible to get 'CHa', depending on the psm parameter.

I suspect that this issue is related to the "baseline" of the text being inconsistent across the line, but I'm not certain.

How can I accurately extract the text from this type of image?

Update - 19th May 2020

After seeing Achintha Ihalage's answer, which doesn't provide any configuration options to tesseract, I explored the psm options.

Since the region of interest is known (in this case, I am using EAST detection to locate the bounding box of the text), the psm config option for tesseract, which in my original code treats the text as a single line, may not be necessary. Running image_to_string against the region of interest given by the bounding box above gives the output

CH

3

which can, of course, be easily processed to get CH3.

Solution

You want to do apply pre-processing to your image before feeding it into tesseract to increase the accuracy of the OCR. I use a combination of PIL and cv2 to do this here because cv2 has good filters for blur/noise removal (dilation, erosion, threshold) and PIL makes it easy to enhance the contrast (distinguish the text from the background) and I wanted to show how pre-processing could be done using either... (use of both together is not 100% necessary though, as shown below). You can write this more elegantly- it's just the general idea.

import cv2
import pytesseract
import numpy as np
from PIL import Image, ImageEnhance


img = cv2.imread('test.jpg')

def cv2_preprocess(image_path):
  img = cv2.imread(image_path)

  # convert to black and white if not already
  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

  # remove noise
  kernel = np.ones((1, 1), np.uint8)
  img = cv2.dilate(img, kernel, iterations=1)
  img = cv2.erode(img, kernel, iterations=1)

  # apply a blur 
  # gaussian noise
  img = cv2.threshold(cv2.GaussianBlur(img, (9, 9), 0), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

  # this can be used for salt and pepper noise (not necessary here)
  #img = cv2.adaptiveThreshold(cv2.medianBlur(img, 7), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)

  cv2.imwrite('new.jpg', img)
  return 'new.jpg'

def pil_enhance(image_path):
  image = Image.open(image_path)
  contrast = ImageEnhance.Contrast(image)
  contrast.enhance(2).save('new2.jpg')
  return 'new2.jpg'


img = cv2.imread(pil_enhance(cv2_preprocess('test.jpg')))


text = pytesseract.image_to_string(img)
print(text)

Output:

CH3

The cv2 pre-process produces an image that looks like this:

The enhancement with PIL gives you:

In this specific example, you can actually stop after the cv2_preprocess step because that is clear enough for the reader:

img = cv2.imread(cv2_preprocess('test.jpg'))
text = pytesseract.image_to_string(img)
print(text)

output:

CH3

But if you are working with things that don't necessarily start with a white background (i.e. grey scaling converts to light grey instead of white)- I have found the PIL step really helps there.

Main point is the methods to increase accuracy of the tesseract typically are:

fix DPI (rescaling)
fix brightness/noise of image
fix tex size/lines (skewing/warping text)

Doing one of these or all three of them will help... but the brightness/noise can be more generalizable than the other two (at least from my experience).