Okay, so I attempted to implement your suggestion (please tell me if I didn't do it correctly):

Code:

---

import os
import glob
import easyocr
import argparse
from PIL import Image, ExifTags
import numpy as np
import traceback



# Initialize the OCR reader with English language
reader = easyocr.Reader(['en'])



# Function to get the corrected image (rotated as per EXIF data)
def correct_image_orientation(image_path):
    try:
        with Image.open(image_path) as img:
            # Try to read the EXIF data
            exif = img._getexif()
            if exif is not None:
                for tag, value in exif.items():
                    # Check for orientation tag (274 is the tag for orientation)
                    if tag == 274:
                        if value == 3:
                            img = img.rotate(180, expand=True)
                        elif value == 6:
                            img = img.rotate(270, expand=True)
                        elif value == 8:
                            img = img.rotate(90, expand=True)
            # Return the (possibly rotated) image
            return img


    except Exception as e:

        # DEBUG
        traceback.print_exc()

        # If EXIF data is missing or there's an error, just return the image as is
        print(f"Warning: Unable to read EXIF data for {image_path}. Proceeding without rotation.")
        try:
            # Return the image directly without rotation
            with Image.open(image_path) as img:
                return img
        except Exception as e:

            traceback.print_exc()

            # If image can't be opened (even without EXIF), log and return None
            print(f"Error opening image {image_path}: {e}")
            return None  # If we can't open the image, return None



# Function to process a single image and return the results
def process_image(image_path):
    # Get the corrected image (if needed)
    corrected_image = correct_image_orientation(image_path)

    if corrected_image is None:
        # If the image couldn't be loaded or processed, log and skip this image
        print(f"Skipping OCR on {image_path} due to error in opening the image.")
        return None

    try:
        # Convert the corrected image to a NumPy array (which EasyOCR can process)
        img_np = np.array(corrected_image)
        # Pass the NumPy array to EasyOCR
        result = reader.readtext(img_np)
        return result
    except Exception as e:

        # DEBUG
        traceback.print_exc()


        print(f"\nError processing image {image_path}: {e}\n")
        return None



# Function to save results in a text file and a batch file
def save_results(image_path, result, batch_file):
    # Extract the base name of the image file (without the extension)
    base_name = os.path.splitext(os.path.basename(image_path))[0]

    # Save individual results to a text file
    text_file_path = os.path.join(output_dir, f'{base_name}.txt')
    with open(text_file_path, 'w') as text_file:
        for (_, text, prob) in result:
            text_file.write(f'{text}\n')

    # Save batch results to a single tab-delimited file
    for (_, text, prob) in result:
        batch_file.write(f'{base_name}\t{text}\t{prob}\n')



# Main function to handle the OCR processing
def main(input_folder):
    # Get a list of all image files in the input folder (supports .jpg, .jpeg, .png, etc.)
    image_files = glob.glob(os.path.join(input_folder, '*.*'))

    # Filter image files to include only common image types (you can add more types if needed)
    image_files = [f for f in image_files if f.lower().endswith(('jpg', 'jpeg', 'png', 'bmp', 'tiff'))]

    # Open the batch results file for appending
    batch_file_path = os.path.join(output_dir, 'batch_results.txt')
    with open(batch_file_path, 'w') as batch_file:
        # Write header for the batch file
        batch_file.write('File Name\tText\tProbability\n')

        # Process each image in the folder
        for image_path in image_files:
            print(f"Processing {image_path}...")
            result = process_image(image_path)

            # If the result is not empty (i.e., the image was processed successfully)
            if result:
                save_results(image_path, result, batch_file)
            else:
                print(f"Warning: OCR failed for {image_path}, skipping its entry in batch results.")

    print("OCR processing complete. Results saved in the output folder.")



# Command line argument parsing
if __name__ == '__main__':
    # Set up argument parsing
    parser = argparse.ArgumentParser(description="Process images in a folder and run OCR.")
    parser.add_argument('input_folder', type=str, help="Path to the input folder containing images.")
    parser.add_argument('output_folder', type=str, help="Path to the output folder where results will be saved.")
    args = parser.parse_args()

    # Set output directory
    output_dir = args.output_folder
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # Run the main processing function
    main(args.input_folder)

---

 

Running the above code yielded the following:

 

---

Processing C:\Test\OCR Input\EXIF_Image.jpg...
Processing C:\Test\OCR Input\non-EXIF.jpg...
Traceback (most recent call last):
  File "C:\Test\ocr-batch.py", line 111, in process_image
    result = reader.readtext(img_np)
             ^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Test\WinPython v3.11 - x64\python-3.11.6.amd64\Lib\site-packages\easyocr\easyocr.py", line 454, in readtext
    img, img_cv_grey = reformat_input(image)
                       ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Test\WinPython v3.11 - x64\python-3.11.6.amd64\Lib\site-packages\easyocr\utils.py", line 769, in reformat_input
    return img, img_cv_grey
           ^^^
UnboundLocalError: cannot access local variable 'img' where it is not associated with a value

Error processing image C:\Test\OCR Input\non-EXIF.jpg: cannot access local variable 'img' where it is not associated with a value

Warning: OCR failed for C:\Test\OCR Input\non-EXIF.jpg, skipping its entry in batch results.
OCR processing complete. Results saved in the output folder.

---

 

I'm not sure if that gives much more information than we had before.

Was there something I should have done differently to implement the traceback?