How To Extract Structured Data From Invoices in Make.com

Introduction

In today's digital landscape, businesses often deal with invoices in various formats. Some invoices are purely text-based, allowing for easy searching and data extraction, while others contain images, making it challenging to extract any meaningful information. This article will guide you through two different methods to effectively extract structured data from invoices using Make.com, catering to both text-based and image-based invoices.

Understanding the Invoice Formats

Invoices can typically be categorized into two types:

1. Text-Based Invoices: These contain searchable text. You can easily highlight, select, and search for specific information within these PDFs. For example, if you press Control + F and search for "invoice," the PDF allows you to find the term seamlessly.

2. Image-Based Invoices: These invoices consist entirely of images, meaning there's no selectable text. As a result, attempting to search through these PDFs using Control + F will yield no results.

It's essential to have a method for handling both types when automating the extraction of invoice data.

Method 1: Extracting Data from Text-Based Invoices

To initiate the extraction process for text-based invoices using Make.com, follow these steps:

1. Retrieve Your Invoice: First, ensure the PDF is accessible in Make.com. In this example, we are using a text-based invoice retrieved from Google Drive, but it could come through email or another source.

2. Convert PDF to Text: Utilize the Dumpling AI module, which allows you to convert the PDF into text format. This process involves reading the contents of the PDF and extracting the relevant text.

3. Pass the Text to an AI Model: Once you have the textual data, you can pass it into a language model (like ChatGPT) and request the structured data you want.

For example, if the invoice contains information about the total amount, you can instruct the AI to identify this data field.

However, it is important to note that text-based extraction methods can fail if the text is not organized correctly within the PDF. If the layout is jumbled, it may lead to inaccurate results.

Method 2: Extracting Data from Image-Based Invoices

When dealing with image-based invoices, the process changes slightly:

1. Download the Image-Based PDF: Make sure to retrieve the invoice that has images embedded within it.

2. Image Analysis with Vision-Based AI: Instead of a simple conversion to text, you would use a more sophisticated approach utilizing multimodal AI. In this case, you would leverage the Dumpling AI "Extract Data from PDF AI," which employs vision-based AI capabilities to interpret the images in the PDF.

3. Configure the AI Prompt: Just like with text extraction, you can send a request to the AI to extract relevant data. For example, you would alter the prompt to specify the pieces of information you're after, such as the total amount due.

When successfully configured, the AI should accurately extract the information. For instance, if the correct total was 785, the AI would return this value.

Using a vision-based approach is recommended for a robust solution that can handle any invoice format—text-based, image-based, or a combination of both.

Conclusion

Whether dealing with text-based or image-based invoices, Make.com provides effective methods for structured data extraction. Leveraging text and multimodal AI tools ensures that your invoice processing is both efficient and reliable.

Keywords

• Structured Data
• Invoice Extraction
• Make.com
• PDF Processing
• Text-Based Invoices
• Image-Based Invoices
• Multimodal AI
• Dumpling AI

FAQ

Q1: What types of invoices can I extract data from in Make.com?
A1: You can extract data from both text-based invoices and image-based invoices using different methods.

Q2: Why is it important to handle both text and image-based invoices?
A2: Different businesses may send invoices in various formats; therefore, having a method to accommodate all types ensures accurate data extraction.

Q3: What tools are recommended for extracting structured data from invoices?
A3: For text-based invoices, you can use modules to convert PDFs to text and then leverage AI models for extraction. For image-based invoices, it’s best to use vision-based AI, like Dumpling AI.

Q4: How reliable is the extraction of data from text-based invoices?
A4: While text extraction can be reliable, it may fail if the text layout is jumbled or improperly formatted.

Q5: What advantages do multimodal AI approaches have over traditional OCR?
A5: Multimodal AI systems are typically more accurate and can interpret both textual and visual data more effectively, making them preferable for complex documents.