CODEWITHSUNDEEP
pythonopencvimage-processingocrtesseract
srikanth2016
srikanth2016

Reputation: 29

OCR not performing well on clean image | Python Pytesseract

I have been working on project which involves extracting text from an image. I have researched that tesseract is one of the best libraries available and I decided to use the same along with opencv. Opencv is needed for image manipulation.

I have been playing a lot with tessaract engine and it does not seems to be giving the expected results to me. I have attached the image as an reference. Output I got is:

1] =501 [

Instead, expected output is

TM10-50%L

What I have done so far:

Are there any other suggestions to improve the algorithm?

Thanks in advance.

Snippet of the code:

import cv2
import sys
import pytesseract
import numpy as np
from PIL import Image

if __name__ == '__main__':
  if len(sys.argv) < 2:
    print('Usage: python ocr_simple.py image.jpg')
    sys.exit(1)

  # Read image path from command line
  imPath = sys.argv[1]
  gray  = cv2.imread(imPath, 0)
  # Blur
  blur  = cv2.GaussianBlur(gray,(9,9), 0)
  # Binarizing
  thres = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 3)
  text = pytesseract.image_to_string(thresh)
  print(text)

Images attached. First image is original image. Original image

Second image is what has been fed to tessaract. Input to tessaract

Upvotes: 2

Views: 1474

Answers (1)

nathancy
nathancy

Reputation: 46590

Before performing OCR on an image, it's important to preprocess the image. The idea is to obtain a processed image where the text to extract is in black with the background in white. For this specific image, we need to obtain the ROI before we can OCR.

To do this, we can convert to grayscale, apply a slight Gaussian blur, then adaptive threshold to obtain a binary image. From here, we can apply morphological closing to merge individual letters together. Next we find contours, filter using contour area filtering, and then extract the ROI. We perform text extraction using the --psm 6 configuration option to assume a single uniform block of text. Take a look here for more options.

Detected ROI

enter image description here

Extracted ROI

enter image description here

Result from Pytesseract OCR

TM10=50%L

Code

import cv2
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Grayscale, Gaussian blur, Adaptive threshold
image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 5, 5)

# Perform morph close to merge letters together
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=3)

# Find contours, contour area filtering, extract ROI
cnts, _ = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]
for c in cnts:
    area = cv2.contourArea(c)
    if area > 1800 and area < 2500:
        x,y,w,h = cv2.boundingRect(c)
        ROI = original[y:y+h, x:x+w]
        cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 3)

# Perform text extraction
ROI = cv2.GaussianBlur(ROI, (3,3), 0)
data = pytesseract.image_to_string(ROI, lang='eng', config='--psm 6')
print(data)

cv2.imshow('ROI', ROI)
cv2.imshow('close', close)
cv2.imshow('image', image)
cv2.waitKey()

Upvotes: 2

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