The /form-processing command allows you to extract form fields and their values using a machine learning model specifically trained for this purpose. Over the last year, LLM models like Claude and OpenAI have eclipsed custom models like this. But every now and then, a custom trained model like this may have greater accuracy. So if you’re struggling with a LLM to do document processing on scanned or PDF forms, give this a try.

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Basic Usage

Process a form by either uploading a file or providing a URL: 
/form-processing
File: [Upload your form file]
or 
/form-processing
URL: [URL to your form file]

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Input Formats

Supported file types include:
  • PDF documents
  • Scanned images (PNG, JPEG)
  • Digital forms

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Output Format

The command returns a JSON object containing:
  1. Field Extractions:
    • Field names and their extracted values
    • Confidence scores for each extraction
    • Field types (e.g., checkbox status, text fields)
  2. Metadata:
    • Processing timestamp
    • File information
    • Model version
Example output: 
{
    "json": {
        "PHARMACY": {
            "fieldValue": "filled_checkbox",
            "confidence": 0.9997177124023438
        },
        "Member ID Number": {
            "fieldValue": "871381692",
            "confidence": 0.9334482550621033
        },
        "Date of Birth:": {
            "fieldValue": "20-MARCH-1960",
            "confidence": 0.9115520119667053
        }
    },
    "table": []
}

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Understanding the Results

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Field Values

  • Text fields: Contains the extracted text
  • Checkboxes: Reports as either “filled_checkbox” or “unfilled_checkbox”
  • Empty fields: May return ”-” or be omitted

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Confidence Scores

  • Range from 0 to 1 (0% to 100% confidence)
  • Higher scores indicate greater confidence in the extraction
  • Generally:
    • greater than 0.9: Very high confidence
    • 0.7-0.9: Good confidence
    • less than 0.7: Lower confidence, may need review
The Form Processing command provides a reliable way to extract data from structured forms. While newer LLM models have broader capabilities, this specialized tool can still outperform them for specific form processing tasks.