Combining Prebuilt Invoice Models with Custom Models: Enhancing Invoice Processing
Introduction:
Extracting information from invoices can be a tedious task, especially when dealing with a high volume of documents. Fortunately, machine learning models, particularly prebuilt models, can automate this process, saving time and reducing errors. However, prebuilt models might not always cater to the specific nuances of your business. This is where the power of custom models comes in, allowing you to enhance the accuracy and efficiency of invoice processing.
Scenario:
Let's say you are a company that processes invoices from various suppliers. You utilize a prebuilt invoice model from a cloud platform to extract key information like invoice number, date, and total amount. While this model works reasonably well, it struggles with extracting specific details like "shipping address" or "discount amount" that are unique to your invoices. This is because the prebuilt model was trained on a generic dataset and lacks the context to identify these specific fields.
Original Code (Python with Google Cloud Vision API):
from google.cloud import vision
client = vision.ImageAnnotatorClient()
with open('invoice.jpg', 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
response = client.document_text_detection(image=image)
for page in response.full_text_annotation.pages:
for block in page.blocks:
for paragraph in block.paragraphs:
for word in paragraph.words:
for symbol in word.symbols:
print(f'{symbol.text} ({symbol.confidence:.1f}%)')
Combining Prebuilt and Custom Models:
To address the limitations of the prebuilt model, you can leverage a custom model trained specifically on your invoice data. This allows you to teach the model to recognize unique fields and formats specific to your invoices. Here's how you can combine the two:
- Utilize the prebuilt model for initial extraction: Use the prebuilt model to extract basic information like invoice number, date, and total amount.
- Train a custom model: Train a custom model on a dataset of your own invoices, focusing on the specific fields you need to extract (e.g., shipping address, discount amount).
- Refine extracted information: Use the custom model to refine the information extracted by the prebuilt model. For example, if the prebuilt model identified a field as "address", the custom model can further classify it as "shipping address" or "billing address" based on your invoice structure.
Benefits:
- Increased Accuracy: Custom models trained on your specific invoice data improve the accuracy of information extraction.
- Enhanced Efficiency: Combining models automates the process, reducing manual effort and time spent on data entry.
- Scalability: This approach is scalable, enabling you to handle a larger volume of invoices without compromising accuracy.
Considerations:
- Dataset Quality: Training a custom model requires a high-quality dataset of labeled invoices.
- Model Maintenance: Regularly update your custom model with new invoice formats and data to ensure its accuracy.
Additional Value:
- Prebuilt models can be used as a starting point for building custom models.
- You can use a combination of prebuilt and custom models to extract different types of information from invoices.
- This approach is particularly useful for businesses with specific invoice formats or requirements.
Conclusion:
Combining prebuilt and custom models offers a powerful and efficient approach to invoice processing. By leveraging the strengths of both, you can achieve higher accuracy and streamline your operations. Remember to invest in quality data and ongoing model maintenance for optimal results.
References:
