改进 tesseract 的图像预处理（视频游戏截图）

Question

I am trying to read text for prices in a video game and am experiencing difficulty in pre-processing the image.

The rest of my code is "complete", as in after the text is extracted I am formatting it and outputting into CSV for later use.

This is what I have come up with so far for the following images, and would like input on other thresholds or pre-processing tools that will make the OCR more accurate.

Raw Image Screenshot

After gamma, denoise on left - binary threshold on right

The text detected

As you can see, it is very close but not perfect. I would like to make it more accurate as I will be processing many frames eventually.

Here is my current code:

import cv2
import pytesseract
import pandas as pd
import numpy as np

# Tells pytesseract where the tesseract environment is installed on local computer
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

img = cv2.imread("./image_frames/frame0.png")

# gamma to darken text to be same opacity?
def adjust_gamma(crop_img, gamma=1.0):
    # build a lookup table mapping the pixel values [0, 255] to
    # their adjusted gamma values
    invGamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** invGamma) * 255
        for i in np.arange(0, 256)]).astype("uint8")
    # apply gamma correction using the lookup table
    return cv2.LUT(crop_img, table)

adjusted = adjust_gamma(crop_img, gamma=0.15)

# grayscale the image
gray = cv2.cvtColor(adjusted, cv2.COLOR_BGR2GRAY)
# denoising image
dst = cv2.fastNlMeansDenoising(gray, None, 10, 10, 10)


# binary threshold
thresh = cv2.threshold(gray, 35, 255, cv2.THRESH_BINARY_INV)[1]


# OCR configurations (3 is default)
config = "--psm 3"

# Just show the image
cv2.imshow("before", gray)
cv2.imshow("before", dst)
cv2.imshow("thresh", thresh)
cv2.waitKey(0)

# Reads text from the image and prints to console
text = pytesseract.image_to_string(thresh, config=config)
# remove double lines
text = text.replace('\n\n','\n')
# remove unicode character
text = text.replace('', '')
print(text)

Any help is appreciated as I am very new to this!

Answer 1

Step#1: Scale the image

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Step#2: Apply adaptive-threshold

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Step#3: Set page-segmentation-mode ( psm ) to 6 (Assume a single uniform block of text.)

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1 Scaling the image:

• The reason is to see the image clearly, since the original image is really small.

•  img = cv2.imread("udQw1.png") img = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)

2 Apply adaptive-threshold

• Generally threshold is applied, but in your image, applying threshold has no effect to the result.

• For different images you may need to set different C and block values.

• For instance for the 1st image:

•  gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 15, 22)

• Result:

  • 

• For instance for the 2nd image:

•  gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 51, 4)

• Result:

  • 

3 Set psm to 6 which assumes the image as a single uniform block of text.

•  txt = pytesseract.image_to_string(thr, config="--psm 6") print(txt)

• Result for the 1st image:

  •  Dragon Claymore 1,388,888,888 mesos. Maple Pyrope Spear 288,888,888 mesos. Element Pierce 488,888,888 mesos. Purple Adventurer Cape 97,777,777 mesos.

• Result for the 2nd image:

  •  Ring of Alchemist 749,999,995 mesos. Dragon Slash Claw 499,999,995 mesos. "Stormcaster Gloves 149,999,995 mesos. Elemental Wand 6 749,999,995 mesos. Big Money Chalr 1 tor 249,999,985 mesos.|

Code for the 1st image:

---

import pytesseract
import cv2

img = cv2.imread("udQw1.png")
img = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
                            cv2.THRESH_BINARY_INV, 15, 22)
txt = pytesseract.image_to_string(thr, config="--psm 6")
print(txt)

Code for the 2nd image:

import pytesseract
import cv2

img = cv2.imread("7Y2yx.png")
img = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
                            cv2.THRESH_BINARY_INV, 51, 4)
txt = pytesseract.image_to_string(thr, config="--psm 6")
print(txt)

Links

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• 1. Simple and adaptive-threhsold
• 2. Page segmentation Modes
• 3. Improving quality of the output