Table of Contents

📄 Understanding Intelligent Document Processing (IDP) in LM-Kit.NET


📄 TL;DR

Intelligent Document Processing (IDP) is an AI-powered approach to automatically capturing, classifying, extracting, and validating information from documents. Unlike traditional OCR or template-based systems, IDP uses language models, computer vision, and semantic understanding to process diverse document types with human-like comprehension. In LM-Kit.NET, IDP capabilities span document ingestion, layout analysis, text extraction, structured data extraction, and classification, all running locally for maximum privacy and performance.


📚 What is Intelligent Document Processing?

Definition: Intelligent Document Processing (IDP) is an umbrella term for technologies that automate the end-to-end understanding of documents, from raw files (PDFs, images, Office documents) to actionable, structured data. IDP combines multiple AI disciplines:

  • Computer Vision for layout detection and visual understanding
  • Optical Character Recognition (OCR) for text extraction from images
  • Natural Language Processing (NLP) for semantic interpretation
  • Machine Learning for classification and validation

The IDP Pipeline

+-----------------------------------------------------------------------------------+
|                  Intelligent Document Processing Pipeline                         |
+-----------------------------------------------------------------------------------+
|                                                                                   |
|  +----------+    +----------+      +----------+    +----------+    +----------+   |
|  | Capture  |    | Classify |      | Extract  |    | Validate |    | Integrate|   |
|  |          |--->|          |  --->|          |--->|          |--->|          |   |
|  | • PDF    |    | • Type   |      | • Fields |    | • Rules  |    | • APIs   |   |
|  | • Image  |    | • Intent |      | • Tables |    | • Logic  |    | • DBs    |   |
|  | • Office |    | • Route  |      | • Layout |    | • Review |    | • Apps   |   |
|  +----------+    +----------+      +----------+    +----------+    +----------+   |
|                                                                                   |
+-----------------------------------------------------------------------------------+

IDP vs Traditional Document Processing

Aspect Traditional (OCR + Templates) Intelligent Document Processing
Document Variety Requires template per format Handles diverse, unseen formats
Layout Changes Breaks with format changes Adapts to layout variations
Understanding Pattern matching only Semantic comprehension
Accuracy High for known templates High across document types
Maintenance Template updates needed Self-adapting models
Complex Documents Struggles with tables, forms Understands structure

🔍 The Five Stages of IDP

1. Document Capture & Ingestion

The entry point: accepting documents in various formats and preparing them for processing.

Supported Formats:

  • PDF Documents: Native text extraction or OCR for scanned PDFs
  • Images: JPEG, PNG, TIFF, BMP with automatic OCR
  • Office Documents: Word (.docx), Excel (.xlsx), PowerPoint (.pptx)
  • HTML/Web Content: Structured markup with embedded content

2. Document Classification

Automatically determining document type, intent, and routing:

  • Type Classification: Invoice, contract, resume, form, report
  • Intent Detection: Payment request, complaint, inquiry, order
  • Language Identification: Automatic language detection for multilingual processing
  • Quality Assessment: Evaluating document quality for processing confidence

3. Information Extraction

The core IDP function: pulling structured data from unstructured content:

  • Key-Value Extraction: Finding specific fields (invoice number, date, amount)
  • Table Extraction: Recognizing and parsing tabular data
  • Entity Recognition: Identifying people, organizations, locations, dates
  • Relationship Extraction: Understanding connections between entities

4. Validation & Verification

Ensuring extracted data meets quality and business rules:

  • Format Validation: Dates, numbers, identifiers match expected patterns
  • Cross-Field Validation: Related fields are consistent (subtotals match total)
  • Business Rules: Domain-specific logic and constraints
  • Confidence Scoring: Flagging low-confidence extractions for review

5. Integration & Action

Delivering processed data to downstream systems:

  • API Integration: RESTful services, webhooks
  • Database Storage: Structured records in SQL/NoSQL databases
  • Workflow Triggers: Initiating business processes
  • Human-in-the-Loop: Routing exceptions for manual review

⚙️ How LM-Kit.NET Implements IDP

LM-Kit.NET provides a comprehensive toolkit for building IDP solutions through several integrated components:

Core Architecture

+--------------------------------------------------------------------------+
|                      LM-Kit.NET IDP Architecture                         |
+--------------------------------------------------------------------------+
|                                                                          |
|  +---------------------------------------------------------------------+ |
|  |                       Document Ingestion                            | |
|  |  Attachment • PdfDocument • OfficeDocument • ImageData              | |
|  +---------------------------------------------------------------------+ |
|                                    |                                     |
|                                    v                                     |
|  +---------------------------------------------------------------------+ |
|  |                     Pre-Processing Layer                            | |
|  |  VlmOcr • LayoutAnalysis • DocumentSplitting • TextChunker          | |
|  +---------------------------------------------------------------------+ |
|                                    |                                     |
|                                    v                                     |
|  +---------------------------------------------------------------------+ |
|  |                      Understanding Layer                            | |
|  |  Categorization • LanguageDetection • SentimentAnalysis             | |
|  +---------------------------------------------------------------------+ |
|                                    |                                     |
|                                    v                                     |
|  +---------------------------------------------------------------------+ |
|  |                       Extraction Layer                              | |
|  |  TextExtraction • NamedEntityRecognition • PiiExtraction            | |
|  +---------------------------------------------------------------------+ |
|                                                                          |
+--------------------------------------------------------------------------+

Document Ingestion

using LMKit.Data;
using LMKit.Document;

// Universal attachment for any document type
var attachment = new Attachment("invoice.pdf");

// Or specific document types for more control
var pdfDoc = new PdfDocument("contract.pdf");
var officeDoc = new OfficeDocument("report.docx");

// Image ingestion with vision capabilities
var imageData = ImageData.FromFile("scanned_form.png");

OCR and Text Recognition

using LMKit.Graphics;
using LMKit.Model;

// Load a vision-capable model
var model = LM.LoadFromModelID("gemma3:4b");

// Create vision-based OCR
var vlmOcr = new VlmOcr(model);

// Extract text from scanned document
var ocrResult = vlmOcr.Execute(
    ImageData.FromFile("scanned_invoice.png"),
    CancellationToken.None
);

Console.WriteLine(ocrResult.Text);

Layout Analysis

using LMKit.Document;
using LMKit.Model;

var model = LM.LoadFromModelID("gemma3:12b");

// Analyze document structure
var layoutAnalyzer = new LayoutAnalysis(model);
layoutAnalyzer.SetContent(new Attachment("complex_form.pdf"));

var layout = layoutAnalyzer.Analyze(CancellationToken.None);

// Access detected regions
foreach (var region in layout.Regions)
{
    Console.WriteLine($"Type: {region.Type}, Bounds: {region.Bounds}");
    Console.WriteLine($"Content: {region.Text}");
}

Document Classification

using LMKit.TextAnalysis;
using LMKit.Model;

var model = LM.LoadFromModelID("gemma3:4b");

// Classify document type
var categorizer = new Categorization(model);
categorizer.Categories.Add("Invoice");
categorizer.Categories.Add("Contract");
categorizer.Categories.Add("Resume");
categorizer.Categories.Add("Report");
categorizer.Categories.Add("Form");

categorizer.SetContent(new Attachment("unknown_document.pdf"));
var result = categorizer.Categorize(CancellationToken.None);

Console.WriteLine($"Document type: {result.Category} ({result.Confidence:P1})");

Structured Data Extraction

using LMKit.Extraction;
using LMKit.Model;

var model = LM.LoadFromModelID("gemma3:12b");
var extractor = new TextExtraction(model);

// Define extraction schema
extractor.Elements.Add(new TextExtractionElement("vendor_name", ElementType.String)
{
    Description = "Company name of the vendor/supplier"
});
extractor.Elements.Add(new TextExtractionElement("invoice_date", ElementType.Date)
{
    Description = "Date the invoice was issued"
});
extractor.Elements.Add(new TextExtractionElement("line_items", ElementType.ObjectArray)
{
    Description = "Individual items on the invoice",
    InnerElements = new List<TextExtractionElement>
    {
        new("description", ElementType.String),
        new("quantity", ElementType.Integer),
        new("unit_price", ElementType.Double),
        new("amount", ElementType.Double)
    }
});
extractor.Elements.Add(new TextExtractionElement("total_amount", ElementType.Double)
{
    Description = "Total amount due",
    IsRequired = true
});

// Process document
extractor.SetContent(new Attachment("invoice.pdf"));
var result = extractor.Parse(CancellationToken.None);

Console.WriteLine(result.Json);

Entity Recognition

using LMKit.TextAnalysis;

var model = LM.LoadFromModelID("gemma3:4b");
var ner = new NamedEntityRecognition(model);

ner.SetContent(new Attachment("contract.pdf"));
var entities = ner.Extract(CancellationToken.None);

foreach (var entity in entities.Entities)
{
    Console.WriteLine($"{entity.Type}: {entity.Text} (confidence: {entity.Confidence:P1})");
}
// Output:
// ORGANIZATION: Acme Corporation (confidence: 98.5%)
// PERSON: John Smith (confidence: 97.2%)
// DATE: January 15, 2024 (confidence: 99.1%)
// MONEY: $50,000 (confidence: 96.8%)

Document Splitting

using LMKit.Extraction;
using LMKit.Model;

// Load a vision-capable model
var model = LM.LoadFromModelID("qwen3-vl:8b");

// Create a document splitter
var splitter = new DocumentSplitting(model)
{
    Guidance = "The file contains a mix of invoices and purchase orders."
};

// Detect logical document boundaries
var result = splitter.Split(new Attachment("scanned_batch.pdf"));

Console.WriteLine($"Found {result.DocumentCount} document(s) (confidence: {result.Confidence:P0})");
foreach (var segment in result.Segments)
{
    Console.WriteLine($"  Pages {segment.StartPage}-{segment.EndPage}: {segment.Label}");
}

🎯 IDP Use Cases

1. Invoice Processing

Automate accounts payable by extracting vendor info, line items, totals, and payment terms from invoices in any format.

2. Contract Analysis

Extract key clauses, parties, dates, obligations, and terms from legal agreements for compliance and risk management.

3. Claims Processing

Parse insurance claims, medical records, and supporting documents to accelerate adjudication.

4. HR Document Processing

Process resumes, applications, and employee documents for hiring, onboarding, and compliance.

5. Loan & Mortgage Processing

Extract borrower information, financial data, and property details from complex mortgage packages.

6. Healthcare Records

Parse patient records, lab results, and clinical notes while maintaining HIPAA compliance with local processing.

7. Mailroom and Batch Scanning

Split multi-page scanned PDFs into individual logical documents (invoices, contracts, ID cards) using vision-based boundary detection, then route each document to the appropriate processing pipeline.


📖 Key Terms

  • Document Capture: The process of acquiring documents from various sources (scanners, email, uploads)
  • Document Splitting: Detecting logical document boundaries within multi-page files using vision models, returning page ranges and labels for each document
  • Layout Analysis: Detecting and understanding the visual structure of a document (headers, paragraphs, tables)
  • OCR (Optical Character Recognition): Converting images of text into machine-readable characters
  • Entity Extraction: Identifying and classifying named entities (people, organizations, dates, amounts)
  • Schema: A predefined structure defining what fields to extract and their data types
  • Confidence Score: A measure of the model's certainty about an extraction or classification
  • Human-in-the-Loop (HITL): A workflow pattern where low-confidence results are routed for human review

📚 Related API Documentation


🔗 Related Glossary Topics


🌐 External Resources


📝 Summary

Intelligent Document Processing (IDP) represents the evolution from simple OCR to comprehensive document understanding. By combining computer vision, OCR, NLP, and language models, IDP systems can capture, classify, extract, validate, and integrate document data with minimal human intervention. In LM-Kit.NET, IDP capabilities span the entire pipeline: from document ingestion through document splitting, layout analysis, classification, entity recognition, and structured extraction, all running locally on-device. This enables enterprises to automate document-heavy workflows while maintaining data privacy, regulatory compliance, and operational efficiency.