Document Analysis

Document analysis is often discussed in the context of AI, large language models, and external cloud services. However, many document processing tasks do not require probabilistic models. When the structure, terminology, and expected document types are known, deterministic approaches can provide fast, explainable, reproducible results that are easy to integrate into production systems.

Over the past several months, we have published a series of technical articles demonstrating these techniques with TX Text Control .NET Server. Four of these concepts are now available as interactive live samples in our online demos.

Rather than reading about the algorithms, developers can now run complete workflows in a browser, inspect original documents, review generated results, and study implementation in the integrated Learn and Code sections.

The new samples cover four important areas of document automation.

Classifying incoming documents. Extracting normalized information from document tables. Mapping independently maintained document templates to application data. Detecting and removing hidden or sensitive information before documents continue through a workflow.

All four samples work with the structure of the documents themselves rather than reducing them to plain, unstructured text.

Document Classification

The new Document Classification sample shows how to classify DOCX documents using configurable, deterministic rules.

Try the live Document Classification sample

It recognizes several common document categories, including invoices, contracts, résumés, reports, and financial documents.

Instead of submitting the document to an AI model, the application analyzes signals found throughout the document. Titles, headings, body text, headers, and footers can all contribute to the classification.

Each signal is evaluated against configurable, weighted rules. The result is more than just a category name. The sample also displays the classification's confidence level, competing category scores, and the strongest evidence that influenced the decision.

This makes the result both explainable and reproducible. Processing the same document with the same classification profile will always produce the same result.

For workflows involving known document classes, this approach is particularly useful because it eliminates dependency on an external AI service and uncertainty about why a particular category was selected.

Learn more

In this article, we explore how to implement document classification without relying on AI. We will discuss deterministic methods that are explainable and suitable for production environments. This approach can be particularly beneficial for organizations that require transparency and control over their classification processes.

Document Classification Without AI: Deterministic, Explainable, and Built for Production in C# .NET

Structured Table Extraction

Tables often contain the most valuable structured information in business documents. However, tables created in Word are designed primarily for presentation, not as normalized data structures.

The Structured Table Extraction sample demonstrates how tables in DOCX documents can be analyzed and converted into normalized JSON.

Try the live Structured Table Extraction sample

This sample includes financial and healthcare documents and can determine the document domain automatically or use a domain selected by the user.

The analysis goes beyond simply reading the contents of individual table cells. It identifies table headers, recognizes variations in column names, maps them to a canonical schema, handles merged cells, infers data types, and distinguishes regular records from totals.

For instance, different documents may use slightly different column names for the same information. A domain-aware mapping layer can normalize these variations before returning the data to the application.

The resulting JSON is displayed alongside the original DOCX document, making it easy to compare the source structure with the normalized output.

This approach is useful when organizations receive documents from different systems or departments containing semantically similar information with slightly different layouts or terminology.

Learn more

In this article, we build a domain-aware table extraction system using TX Text Control in C# .NET. The system automatically detects the table's domain, understands column semantics, and produces clean JSON output suitable for analytics systems, data imports, or AI pipelines.

Extracting Structured Table Data from DOCX Word Documents in C# .NET with Domain-Aware Table Detection

Form Field Mapping

Document automation systems often evolve in two distinct ways. Application developers maintain data models and JSON schemas, while document authors maintain templates containing form fields, merge fields, and other placeholders. Over time, the names used by each group can diverge.

The Form Field Mapping sample shows how these independently maintained structures can be aligned automatically.

Try the live Form Field Mapping sample

The workflow makes the problem immediately visible. First, users can merge the supplied JSON data into the document without any mapping. Fields whose names do not match will remain unresolved.

Then, the mapping algorithm can be enabled, and the document can be merged again. The corrected result is shown directly in the TX Text Control Document Editor.

The mapper does not depend on a single comparison technique. It combines normalized field-name matching, token comparison, containment checks, and Levenshtein similarity.

Candidate mappings receive confidence scores that are compared against an adjustable threshold. Equally important, the sample reveals the mapping decision process. Developers can see which fields were accepted or skipped and why a particular mapping was selected.

This enables applications to automate obvious mappings while retaining control over ambiguous cases.

Learn more

In this article, we will explore how to automatically map TX Text Control form fields to JSON data in a .NET C# application. This process can help streamline data handling and improve the efficiency of your application when working with form fields in TX Text Control.

Automatically Mapping TX Text Control Form Fields to JSON Data in .NET C#

Document Sanitization

Documents often contain more information than is visible on the page. Even when the visible result appears clean, metadata, populated form fields, internal comments, tracked changes, revision information, and embedded content can remain inside a document.

The new Document Sanitization live sample shows how to detect and remove this hidden information as part of a controlled document-processing pipeline.

Try the live Document Sanitization sample

The workflow intentionally separates detection from removal. First, the document is analyzed without modification. Then, the application creates a report showing the sanitizable information found in the document. This can include metadata, populated form fields, comments, tracked changes, and embedded information.

The actual sanitization step is performed only after this inspection.

The two-step approach is important for automated document pipelines because it provides transparency and traceability. Applications can determine what was present in a document, decide if sanitization is required, perform the cleanup, and verify the resulting state.

The sample demonstrates how TX Text Control can remove or reset information, such as document properties, form field values, comments, tracked changes, and embedded files, before exporting or transferring a document to another system.

This is particularly relevant in legal, healthcare, government, and other regulated workflows. It is also increasingly important when documents are passed to external AI services. Sanitizing a document before it leaves a controlled environment ensures that only the intended information is shared for downstream processing.

Learn more

This article explores the importance of data sanitization in document processing pipelines and explains how to use TX Text Control effectively to sanitize data in C# .NET applications. Additionally, we will discuss common challenges associated with handling user-generated content and offer practical solutions to help you maintain the integrity and security of your document processing workflows.

Sanitizing Data in Document Pipelines: A Practical Approach with TX Text Control in C# .NET

Building Blocks for Document Automation

Though each sample showcases a distinct technique, collectively, they constitute essential building blocks for a comprehensive document-processing architecture.

First, an incoming document is classified to determine its type. Then, relevant structures, such as tables, can be located and converted into normalized application data. Document templates and application schemas can be aligned so that the data can reliably be used in subsequent document processes.

Sanitization adds another important dimension to this workflow. Rather than being limited to one specific stage, sanitization can act as a control point wherever documents cross a trust boundary, are exported, shared externally, or sent to another service.

Therefore, a document automation pipeline could combine these techniques as follows:

Document classification → Structured data extraction → Template and data mapping → Downstream processing

Document sanitization would be applied wherever the workflow requires a clean and controlled document state.

These techniques can be used independently or combined according to an application's requirements. Typical scenarios include document routing, reporting systems, healthcare document processing, financial workflows, template normalization, compliance applications, AI preprocessing, and automated document generation.

Deterministic and Explainable Document Processing

Not every document problem requires an AI model. For well-understood document structures and domains, deterministic algorithms offer several key advantages. These advantages include reproducible results, inspectable and adjustable rules, explainable confidence scores, and processing that remains under the application's control.

TX Text Control provides access to the underlying structure of documents, including text, sections, headers, footers, tables, fields, comments, tracked changes, metadata, and other elements. Applications can use this structure as the basis for specialized analysis and processing algorithms while working with the original document.

AI can complement these techniques where semantic interpretation or highly variable documents are involved. However, deterministic processing remains an essential tool for workflows that require predictability, transparency, auditability, and control.

Try the New Document Analysis Samples

In the TX Text Control live demos, open the Document Analysis category and explore the workflows directly. These samples serve as practical starting points for creating document routing, structured data extraction, template normalization, document sanitization, compliance workflows, AI preprocessing, and other document-aware automation scenarios within ASP.NET Core applications.

More document analysis samples will be added as we continue to expand the new educational demo platform.

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