Help: Parsing Product Nutrition Info from NFP Images

[gautam-ergo]

I'm working on a school project that involves extracting product nutrition information from images of Nutrition Facts Panels (NFPs) using Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques. I've encountered some challenges in parsing the extracted text data and would greatly appreciate any guidance.

I tried to use paddleocr python package to do the text extraction after doing some image pre-processing using cv2 (no gpu), but was not successful. Are there any specific params to set up for this kind of use-case ?

a sample nfp image used as input:

TIA

[GreatV]

To address your issue of parsing product nutrition information from Nutrition Facts Panel (NFP) images using PaddleOCR and OpenCV, here are some points and recommendations:

1. Image Format Requirement for PaddleOCR

PaddleOCR expects input images in RGB format (not BGR). Since OpenCV loads images in BGR format by default, you need to convert the image to RGB before passing it to PaddleOCR. You can do this using the following code:

import cv2

# Load the image using OpenCV
image = cv2.imread('path_to_your_image.jpg')

# Convert the image from BGR to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

This aligns with the discussion in the provided reference: PaddleOCR expects images in RGB or BGR format?.

2. Image Pre-Processing with OpenCV

To improve OCR accuracy, pre-processing is crucial. Here are some steps you can follow:

• Grayscale Conversion: Convert the image to grayscale to simplify the data.

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

• Thresholding: Apply adaptive or binary thresholding to enhance contrast between text and background.

# Adaptive Thresholding
thresh_image = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

• Denoising: Use GaussianBlur or median filtering to reduce noise.

blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)

• Resizing: Ensure the image is of sufficient resolution. PaddleOCR performs better with clear and high-resolution inputs.

3. OCR Configuration in PaddleOCR

When using PaddleOCR, ensure that:

• You set the correct detection and recognition models (ocr_db_crnn is a good starting option for general OCR tasks).
• Use the paddleocr Python package with default parameters for the first run and adjust based on results.

Here’s a basic example of how to run PaddleOCR after pre-processing:

from paddleocr import PaddleOCR

# Initialize PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang='en')  # Set language as needed

# Run OCR on the pre-processed image
results = ocr.ocr(image_rgb)

# Print results
for line in results[0]:
    print(line)

4. Handling Nutrition Facts Panel Specific Challenges

NFP images can have structured layouts, which may require additional processing:

• Table Detection: If the NFP is in a tabular format, you may need to detect and segment the table before running OCR.
• Post-OCR Parsing: After text extraction, use NLP techniques to structure the data (e.g., mapping "Calories", "Total Fat", etc., to their respective values).

5. Common Issues and Debugging Tips

• Low OCR Accuracy: Check image quality and pre-processing. You may also experiment with PaddleOCR’s model parameters or even fine-tune the OCR model on NFP-specific data.
• Incorrect Text Alignment: Use image segmentation or cropping to isolate specific regions of the NFP.
• Data Formatting: Post-process the OCR output using Python libraries like re (regular expressions) or pandas for structured data.

Summary

• Convert your images to RGB before feeding them into PaddleOCR.
• Perform pre-processing steps like grayscale conversion, thresholding, and denoising to improve OCR accuracy.
• Use PaddleOCR with default settings initially, and refine based on your specific needs.
• For structured data like NFPs, consider table detection and post-OCR parsing for better results.

Feel free to share specific issues or errors you encounter for further assistance!

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