ChatGPT Plugin for Aspose.PDF
How to Extract Structured Data from PDF Using ChatGPT and .NET

How to Extract Structured Data from PDF Using ChatGPT and .NET

Unlock advanced automation and AI-powered workflows in your .NET applications by extracting structured data (such as tables, forms, or entities) from PDFs and converting it to machine-readable formats. This detailed guide walks through all steps—from text extraction to intelligent data parsing using ChatGPT.

Introduction

Structured data extraction from PDF documents is a critical requirement for business intelligence, reporting, and automation scenarios. While Aspose.PDF.Plugin enables robust text extraction in .NET, coupling it with ChatGPT allows you to parse, categorize, and format information as JSON, CSV, or domain objects.

Common Use Cases:

  • Invoice data extraction for accounting automation
  • Parsing tables from research papers
  • Transforming scanned forms into structured records

Step 1: Extract Text or Table Content from PDF

Begin by using the TextExtractor or, for tabular data, specialized options in Aspose.PDF.Plugin.

using Aspose.Pdf.Plugins;

var inputPath = @"C:\Docs\invoice.pdf";
var extractor = new TextExtractor();
var options = new TextExtractorOptions();
options.AddInput(new FileDataSource(inputPath));
var resultContainer = extractor.Process(options);
string rawText = resultContainer.ResultCollection[0].ToString();

Step 2: Prepare and Send Prompts to ChatGPT

You can instruct ChatGPT to parse and return the data in a structured format such as JSON or CSV.

string apiKey = "YOUR_OPENAI_API_KEY";
string prompt = $"Extract the following invoice data as JSON: {rawText}";
// Use HttpClient as in previous examples

  • Tips for Better Results:

    • Use clear, explicit prompts: “Extract a table of item descriptions, prices, and totals as JSON.”
    • For large PDFs, extract and send text in logical segments (e.g., one table at a time).

Step 3: Parse and Validate AI Output

After getting ChatGPT’s response, parse the structured data using a JSON (or CSV) parser:

// Assume jsonData is a JSON string received from ChatGPT
var structuredData = JsonConvert.DeserializeObject<List<InvoiceItem>>(jsonData);

public class InvoiceItem
{
    public string Description { get; set; }
    public decimal Price { get; set; }
    public int Quantity { get; set; }
    public decimal Total { get; set; }
}

Validation Steps:

  • Check for valid data types (numeric, date, etc.)
  • Log or flag incomplete/ambiguous data for review

Step 4: Save or Use Extracted Data

  • Store the structured results in a database, Excel file, or downstream processing system.
  • Optionally, use Aspose.PDF.Plugin’s TableGenerator to inject structured data back into a summary PDF or report.

Advanced Scenarios & Troubleshooting

  • Batch Extraction:

    • Loop through multiple PDFs and aggregate structured data from all documents.

  • Combining OCR:

    • For scanned PDFs, use OCR plugins first before text extraction.

  • Error Handling:

    • Catch and log API errors, invalid JSON responses, and unstructured fragments.

Best Practices for Accuracy & Compliance

  • Pre-clean PDF text before sending to ChatGPT to remove headers/footers.
  • Avoid sending sensitive documents unless using secure/authorized AI endpoints.
  • For critical data extraction, use a post-processing validation step.

FAQ: Structured Data Extraction with ChatGPT

Q: What types of structured data can I extract from PDFs? A: Tables, lists, named fields, and regular patterns (like dates, amounts, IDs).

Q: Can this method process multiple PDFs at once? A: Yes. Batch extraction is supported—loop through your PDF set and aggregate results.

Q: Is ChatGPT always accurate with tables and numbers? A: For best results, use precise prompts and validate all outputs in code.

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