I'll assume that this is a caching issue, otherwise your first call to the system would not produce an image at all. A quick and dirty hack is to just force your image not to be cached by adding a random query string after the image URL within the result.html:

<img src ="/static/fig1.png?{{no_cache}}"/>

And then just call it from your Flask app as:

return render_template("result.html", no_cache=time.time())  # import time, of course

This, kind of, defeats the purpose of static content - if you're always going to render the image based on user input there is little point in storing it at all - just create a different Flask endpoint that will render the image directly to the user without ever storing it and call that instead of the image's static URL.

You need to know the followings:

• Image-thresholding

• Improving the quality of the output

You have already know the page-segmentation-modes, therefore I don't need to recommend it.

One way to recognize the text is applying threshold to the image. You have three-options

• Simple-threshold

• Adaptive-threshold

• Threshold using cv2.inRange

You have already applied simple-threshold and couldn't get the desired result. Next you can apply adaptive-threshold which will be:

Now if you read it with your current configuration:

607

Unfortunately digit 9 is not recognized.

If we try inRange threshold:

Now if you read it with your current configuration:

607

Code:

import cv2
import numpy as np
import pytesseract

# Load the image
img = cv2.imread("EkXoW.jpg")

# Convert to the gray-scale
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Convert to the HSV color
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Adaptive-threshold
thr = cv2.adaptiveThreshold(gry, 255,
                            cv2.ADAPTIVE_THRESH_MEAN_C,
                            cv2.THRESH_BINARY, 13, 10)

# Threshold-in-range
thr_in_range = cv2.inRange(hsv,
                           np.array([0, 0, 0]),
                           np.array([179, 255, 185]))

# OCR
print("Threshold-in-range result")
print(pytesseract.image_to_string(thr_in_range,
                                  config="--psm 13 --oem 1 -c tessedit_char_whitelist=0123456789"))

print("Adaptive threshold result")
print(pytesseract.image_to_string(thr,
                                  config="--psm 13 --oem 1 -c tessedit_char_whitelist=0123456789"))

# Display
cv2.imshow("Threshold-in-range result", thr_in_range)
cv2.imshow("Adaptive threshold result", thr)
cv2.waitKey(0)

You can get the same results in 0.3.7

This answer is more of a small-tutorial for image-processing in text recognition than a complete-answer. The reason is the digit-nine is not recognized. Still, I think the explanation contains brief information and useful links which might encourage you to recognize the digit-nine.