Thanks for the response, Can you please help me, how can I detect text in
my use case?
Regards,
Shaik Abdul Kalam
On Thursday, August 22, 2024 at 1:47:24 PM UTC+5:30 zdenop wrote:
> Tesseract is the OCR engine and it is not a text detection tool.
> If you pass just blue button to tesseract, it has no problem to extract
> text:
>
> tesseract blue_button.png -
> Sign in
>
>
> Zdenko
>
>
> št 22. 8. 2024 o 9:11 Abdul Kalam Shaik <shaikabdu...@gmail.com>
> napísal(a):
>
>> Thanks Ger for your response. So, my use case is like when ever there is
>> a colored background I'm unable to detect the text. Attached few use cases
>> where I was facing difficulty in detecting the text.
>>
>> Regards,
>>
>> Shaik Abdul Kalam.
>>
>> On Tuesday, August 20, 2024 at 4:13:42 PM UTC+5:30 ger.h...@gmail.com
>> wrote:
>>
>>> Generally, it is best to convert to greyscale with black text on white
>>> background. Seems you tried that so questions remain.
>>> Please include one or two sample images which exhibits your problem, so
>>> folks around here have something to test against.
>>>
>>> Ciao,
>>>
>>> Ger
>>>
>>> On Mon, 19 Aug 2024, 18:45 Abdul Kalam Shaik, <shaikabdu...@gmail.com>
>>> wrote:
>>>
>>>> Hello,
>>>>
>>>> I am encountering an issue with Tesseract OCR when trying to detect
>>>> white text on a blue background. Despite various preprocessing techniques,
>>>> the OCR is not accurately recognizing the text on this specific background.
>>>>
>>>> *Details:*
>>>>
>>>> Tesseract Version: tesseract v5.0.0-alpha.20210506
>>>> Language Pack: English
>>>> *Image Characteristics:*
>>>> Background color: Blue
>>>> Text color: White
>>>> Image resolution: 1920X1080P
>>>> Image format:PNG
>>>> *Preprocessing Techniques Applied:*
>>>> 1. Grayscale conversion
>>>> 2. Contrast adjustment
>>>> 3. Binary thresholding
>>>> 4. Inversion of the image
>>>> 5. Morphological operations
>>>> 6. Increase Contrast
>>>> 7. ROI
>>>> 8. Convert the image to the HSV color space, Create a mask to isolate
>>>> blue regions,Invert the mask to focus on the text and Using the mask to
>>>> extract the white text
>>>> * Script/Code Used:*
>>>> import cv2
>>>> import pytesseract
>>>> import pyautogui
>>>> import time
>>>> import numpy as np
>>>>
>>>> # Specify the path to the Tesseract executable if not in PATH
>>>> pytesseract.pytesseract.tesseract_cmd = r'C:\Program
>>>> Files\Tesseract-OCR\tesseract.exe'
>>>>
>>>>
>>>> def preprocess_image_gray(image):
>>>> # Convert to grayscale
>>>> gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
>>>> cv2.imshow("Gray Scale Image", gray)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> return gray
>>>>
>>>>
>>>> def preprocess_image_increase_contrast(image):
>>>> # Increase contrast
>>>> contrast = cv2.convertScaleAbs(image, alpha=1.5, beta=0)
>>>> cv2.imshow("Increase contrast", contrast)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> return contrast
>>>>
>>>>
>>>> def preprocess_image_gaussian_blur(image):
>>>> # Apply Gaussian blur
>>>> blurred = cv2.GaussianBlur(image, (5, 5), 0)
>>>> cv2.imshow("GaussianBlur", blurred)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> return blurred
>>>>
>>>>
>>>> def preprocess_image_edge_detection(image):
>>>> # Perform edge detection
>>>> edged = cv2.Canny(image, 50, 150)
>>>> cv2.imshow("edge detection", edged)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> return edged
>>>>
>>>>
>>>> def preprocess_image_inverted(image):
>>>> # Invert the image
>>>> inverted_image = cv2.bitwise_not(image)
>>>> cv2.imshow("Inverted Image", inverted_image)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>>
>>>> return inverted_image
>>>>
>>>>
>>>> def preprocess_image_dialte_edges(image):
>>>> # Dilate the edges
>>>> dilated = cv2.dilate(image, None, iterations=2)
>>>> cv2.imshow("dilate", dilated)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>>
>>>> # Bitwise-AND mask and original image
>>>> result = cv2.bitwise_and(image, image, mask=dilated)
>>>> cv2.imshow("Bitwise-AND mask and original image", result)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>>
>>>> # Invert the image
>>>> inverted_image = cv2.bitwise_not(result)
>>>> cv2.imshow("Inverted Image", inverted_image)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>>
>>>> return inverted_image
>>>>
>>>>
>>>> def perform_ocr(image_path, text_to_find=None, config="--psm 6 --oem
>>>> 3", preprocess_func=preprocess_image_gray):
>>>> global ocr_results
>>>> try:
>>>> image = cv2.imread(image_path)
>>>> image_preprocessed = preprocess_func(image)
>>>> image_rgb = cv2.cvtColor(image_preprocessed, cv2.COLOR_BGR2RGB)
>>>> ocr_data = pytesseract.image_to_data(image_rgb,
>>>> output_type=pytesseract.Output.DICT, config=config)
>>>>
>>>> if text_to_find is not None and not isinstance(text_to_find,
>>>> list):
>>>> text_to_find = [text_to_find]
>>>>
>>>> ocr_results = []
>>>> for i in range(len(ocr_data['text'])):
>>>> text = ocr_data['text'][i].strip()
>>>> if not text:
>>>> continue
>>>>
>>>> confidence = float(ocr_data['conf'][i]) / 100.0 # Convert
>>>> confidence to decimal
>>>> if confidence < 0.2: # Ignore results with confidence less
>>>> than 0.5
>>>> continue
>>>>
>>>> bbox = {
>>>> "text": text,
>>>> "left": ocr_data['left'][i],
>>>> "right": ocr_data['left'][i] + ocr_data['width'][i],
>>>> "top": ocr_data['top'][i],
>>>> "bottom": ocr_data['top'][i] + ocr_data['height'][i],
>>>> "confidence": float(ocr_data['conf'][i]) / 100.0 #
>>>> Convert confidence to decimal
>>>> }
>>>> ocr_results.append(bbox)
>>>> except Exception as e:
>>>> print(f"An error occurred in the main function: {e}")
>>>>
>>>> return ocr_results
>>>>
>>>>
>>>> def draw_boxes(image_path, ocr_results, output_image_path):
>>>> image = cv2.imread(image_path)
>>>>
>>>> for result in ocr_results:
>>>> x, y, w, h = result['left'], result['top'], result['right'] -
>>>> result['left'], result['bottom'] - result['top']
>>>> cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
>>>> distance_text = f"{result['text']} ({result['confidence']:.2f})"
>>>> if 'distance' in result:
>>>> distance_text += f" ({result['distance']:.2f})"
>>>> cv2.putText(image, distance_text, (x, y - 10),
>>>> cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
>>>>
>>>> cv2.imwrite(output_image_path, image)
>>>>
>>>>
>>>> def increase_brightness(img, value=50):
>>>> hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
>>>> h, s, v = cv2.split(hsv)
>>>>
>>>> v = cv2.add(v, value)
>>>> v[v > 255] = 255
>>>> v[v < 0] = 0
>>>>
>>>> final_hsv = cv2.merge((h, s, v))
>>>> brightened_img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
>>>> return brightened_img
>>>>
>>>>
>>>> def isolate_white_text_on_blue(image):
>>>> # Increase brightness
>>>> brightened = increase_brightness(image, value=60)
>>>> cv2.imshow("Brightened image", brightened)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> # Convert the image to the HSV color space
>>>> hsv = cv2.cvtColor(brightened, cv2.COLOR_BGR2HSV)
>>>> cv2.imshow("HSV converted image", hsv)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> # Define the range of blue colors in HSV
>>>> lower_blue = np.array([100, 150, 0])
>>>> upper_blue = np.array([140, 255, 255])
>>>>
>>>> # Create a mask to isolate blue regions
>>>> blue_mask = cv2.inRange(hsv, lower_blue, upper_blue)
>>>> cv2.imshow("Blue Mask image", blue_mask)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> # Invert the mask to focus on the text
>>>> blue_mask_inv = cv2.bitwise_not(blue_mask)
>>>> cv2.imshow(" Inverted Mask image", blue_mask_inv)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>> # Use the mask to extract the white text
>>>> white_text_on_blue = cv2.bitwise_and(brightened, brightened,
>>>> mask=blue_mask_inv)
>>>> cv2.imshow("White Text image", blue_mask_inv)
>>>> cv2.waitKey(0)
>>>> cv2.destroyAllWindows()
>>>>
>>>> return white_text_on_blue
>>>>
>>>>
>>>> def move_and_click(ocr_results):
>>>> for result in ocr_results:
>>>> x_center = (result['left'] + result['right']) // 2
>>>> y_center = (result['top'] + result['bottom']) // 2
>>>> pyautogui.moveTo(x_center, y_center)
>>>> pyautogui.click()
>>>> time.sleep(1) # Sleep for a second between clicks for safety
>>>>
>>>>
>>>> def main():
>>>> image_path = 'path-to-image.png'
>>>> output_json_path = 'path-to-image.json'
>>>> output_image_path = 'path-to-outputimage.png'
>>>>
>>>> text_to_find = []
>>>>
>>>> ocr_results_gray = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=preprocess_image_gray)
>>>> print(f"OCR gray results are: {ocr_results_gray}")
>>>> ocr_results_contrast = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=preprocess_image_increase_contrast)
>>>> print(f"OCR contrast results are: {ocr_results_contrast}")
>>>> ocr_results_gaussian = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=preprocess_image_gaussian_blur)
>>>> print(f"OCR gaussian results are: {ocr_results_gaussian}")
>>>> ocr_results_edge = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=preprocess_image_edge_detection)
>>>> print(f"OCR edge results are: {ocr_results_edge}")
>>>> ocr_results_dialte = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=preprocess_image_dialte_edges)
>>>> print(f"OCR Dialte results are: {ocr_results_dialte}")
>>>> ocr_results_invert = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=preprocess_image_inverted)
>>>> print(f"OCR Invert results are: {ocr_results_invert}")
>>>> ocr_results_isolate = perform_ocr(image_path, text_to_find,
>>>> config="--psm 11 --oem 3",
>>>>
>>>> preprocess_func=isolate_white_text_on_blue)
>>>> print(f"OCR Isolate results are: {ocr_results_isolate}")
>>>>
>>>> ocr_results = []
>>>> if isinstance(ocr_results_gray, list) and
>>>> isinstance(ocr_results_isolate, list) and isinstance(ocr_results_invert,
>>>>
>>>> list) and isinstance(
>>>> ocr_results_contrast, list) and
>>>> isinstance(ocr_results_gaussian, list) and isinstance(ocr_results_dialte,
>>>>
>>>> list) and isinstance(
>>>> ocr_results_edge, list):
>>>> ocr_results = ocr_results_isolate + ocr_results_gray +
>>>> ocr_results_contrast + ocr_results_gaussian + ocr_results_dialte +
>>>> ocr_results_edge + ocr_results_invert
>>>> else:
>>>> print("OCR results are not in the expected list format.")
>>>>
>>>>
>>>> for i, result in enumerate(ocr_results, start=1):
>>>> bounding_box_info = (
>>>> f"Bounding box: Text = {result['text']}, Left =
>>>> {result['left']}, Top = {result['top']}, "
>>>> f"Right = {result['right']}, Bottom = {result['bottom']},
>>>> Confidence = {result['confidence']:.2f}"
>>>> )
>>>> print(bounding_box_info)
>>>> print()
>>>>
>>>> draw_boxes(image_path, ocr_results, output_image_path)
>>>>
>>>> print(ocr_results)
>>>>
>>>>
>>>> if __name__ == "__main__":
>>>> main()
>>>>
>>>> *Issue:*
>>>> Despite trying the above preprocessing techniques, the OCR output is
>>>> still missing or incorrectly recognizing the text on the blue background.
>>>> I
>>>> have also tried adjusting the thresholding and brightness levels, but
>>>> without success.
>>>>
>>>> *Question:*
>>>> Could anyone provide suggestions on additional preprocessing techniques
>>>> or modifications to the Tesseract OCR settings that might help improve the
>>>> detection accuracy for white text on a blue background or any other
>>>> colored
>>>> background?
>>>>
>>>> Thank you in advance for your assistance!
>>>>
>>>> --
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>>>> https://groups.google.com/d/msgid/tesseract-ocr/0ce2e54e-1f1a-4fca-8c74-286c9641509en%40googlegroups.com
>>>>
>>>> <https://groups.google.com/d/msgid/tesseract-ocr/0ce2e54e-1f1a-4fca-8c74-286c9641509en%40googlegroups.com?utm_medium=email&utm_source=footer>
>>>> .
>>>>
>>> --
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>>
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>>
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