Add a basic Pythion/OpenCV-based OCR script

bin/ocr.py

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+import cv2
+import pytesseract
+from PIL import Image
+import json
+
+def crop_and_ocr(image_path, top_coords, bottom_coords):
+    # Read the image using OpenCV
+    image = cv2.imread(image_path)
+
+    # Convert to grayscale
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Crop function
+    def crop_image(image, coords):
+        x, y, w, h = coords
+        return image[y:y+h, x:x+w]
+
+    # OCR function
+    def ocr_cropped_image(cropped_image):
+        pil_image = Image.fromarray(cropped_image)
+        return pytesseract.image_to_string(pil_image, lang='eng', config='--psm 6').strip()
+
+    # Crop images based on the coordinates
+    top_cropped = crop_image(gray_image, top_coords)
+    bottom_cropped = crop_image(gray_image, bottom_coords)
+
+    # Perform OCR
+    top_text = ocr_cropped_image(top_cropped)
+    bottom_text = ocr_cropped_image(bottom_cropped)
+
+    # Combine the text into a JSON object
+    ocr_results = {
+        "top_box_text": top_text,
+        "bottom_box_text": bottom_text
+    }
+
+    return json.dumps(ocr_results, indent=4)
+
+# The coordinates for the boxes would be passed along with the image path when calling the function
+image_path = 'screenshot.jpg'  # Replace with your image file path
+top_box_coords = (715, 54, 962 - 715, 91 - 54)
+bottom_box_coords = (275, 415, 820 - 275, 475 - 415)
+
+# Call the function and print the results
+results_json = crop_and_ocr(image_path, top_box_coords, bottom_box_coords)
+print(results_json)

requirements.txt

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+opencv-python-headless>=4
+Pillow>=9
+pytesseract>=0
+numpy>=1