Invoice Data Extraction using OCR & AI

In today’s business environment managing invoices efficiently is crucial. Invoice data extraction using OCR and AI is a powerful solution for business.

OCR & AI

OCR (Optical Character Recognition) is the technology that scans and converts physical (Image) documents into digital text. It allows businesses to extract data from invoices quickly.

Meanwhile, AI complements this system via analyzing the extracted information for accuracy. Combining OCR & AI makes invoice and bill management easier.

Benefits of OCR & AI

These are the some benefits of using OCR & AI:

• It reduces manual data entry.
• It reduces time.
• Minimizing the error.
• Reduce paperwork, and improve data accuracy and the approval process.

Checkout our Invoice Data Extractions modules

Magento 2 AI OCR Extension

Odoo AI-OCR Document Digitization

Methods of Invoice Data Extraction

There are two methods for Invoice data extraction:

• OCR engine + LLM: This method involves text extraction using an OCR engine like Tesseract or EasyOCR, and LLM extracts the required information.
• Vision LLM: You can upload the document image directly to Vision LLM, and it will give the formatted information you need.

OCR engine + LLM in OCR & AI

This is the old method to extract the required information from the invoice or document. Here, we can extract the text from the document using open-source.

OCR engines like – tesseract, easyocr, etc, or OCR APIs also available from Microsoft, Google, etc. After extracting text we can extract required information by LLM.

Here we can use any LLM like gpt-4o, gpt-4o-mini or open-source models like llama3.1 405b, 70b. We can also run quantized small models which we can run locally LLMs on Ollama.

Small Models: llama3.1 7b, qwen2.5 7b, mistral 7b, etc. These models can work on simple invoices.

Pros

• Flexibility: You can choose any OCR tools and LLMs, allowing customization based on specific needs.
• Open Source Options: Many OCR engines and LLMs are open-source, reducing costs and allowing for greater experimentation.
• Local Deployment: Smaller models can be run locally through tools like Ollama, enhancing data privacy and reducing cloud dependency.

Cons

• Performance Variability: The accuracy of the output can vary depending on the quality of the OCR engine and the model used.
• Processing Time: The two-step process of extracting and analyzing text with an LLM may slow data retrieval.
• Model Limitations: Smaller models like llama3.1 7b, may struggle with complex invoices or documents.

Vision LLM in OCR & AI

In recent times vision based LLM models have evolved and it is very powerful now, you can upload the document image directly. LLMs like – gpt-4o, gpt-4o-mini, gemini-1.5-pro, llama3.2 90b vision

There is no need an OCR engine because it has its own optical recognition(OCR) system. We can directly upload documents and retrieve the required information.

Pros

• Accuracy: It is more accurate than OCR engine + LLM method.
• Speed: It is faster than OCR + LLM because there is no need to extract the text.
• Complex Invoices: It can easily extract the required information from complex invoices.

Cons

• Limited models: There are limited Vision LLMs as compared to text LLMs. Also, open-sourced vision models are limited.
• Cost: The API cost of these models is very high compared to text LLMs. Suppose, we use an open-source model like llama3.2 90b vision locally that also requires heavy resources.
• Small models: Small-size vision models like llama 3.2 11b vision may give inaccurate results in complex invoices.

Select the cost-effective approach or method based on the complexity of Invoice or Document.

Conclusion

Businesses can transform their invoice management processes significantly by using Invoice OCR. This integration leads to smarter, more efficient operations that drive growth and success.