Document Analysis

Where It's Applied

In my practice, I developed a comprehensive system for analyzing and extracting data from complex documents — contracts, invoices, reports, technical documentation, scanned archives, and other files. The system processes both digital PDFs and scanned images, extracts structured data, identifies signatures and tables, creates automatic table of contents, and indexes documents for fast semantic search. This enables companies to automate processing of large document volumes that previously required manual review and data entry.

Who Will Benefit

I recommend this solution to law firms working with contracts needing quick specific clause location or condition comparison. Banks and financial institutions for processing credit documents, contracts, and invoices — automating amount, deadline, and condition extraction. Logistics companies for processing bills of lading, invoices, and customs documents. Government agencies and archives for digitizing and indexing historical documents. Insurance companies for analyzing claims and policies. Medical institutions for processing medical records and patient documents (with confidentiality compliance).

Technologies

Local OCR with DeepSeek OCR

I use DeepSeek OCR — a high-accuracy text recognition model developed by DeepSeek that runs locally on my infrastructure. This is critical for confidential documents — no data leaves your servers, everything is processed on your own server. DeepSeek OCR supports many languages, handles handwritten text well, works with various fonts and image distortions.

Advantage over cloud services (Google Cloud Vision, Azure) — no internet dependency, complete confidentiality, no page limits, and significantly cheaper for large volumes.

Image Preprocessing and Preparation

Before OCR processing, documents undergo a series of treatments: denoising (removing artifacts) for scan quality improvement, contrast enhancement, skew correction (deskewing), and multipage document separation. I use OpenCV and Pillow for these operations. In practice, good preprocessing improves OCR accuracy by 5-15%, especially for old or poorly scanned documents.

Table Detection and Classification

I apply specialized models for detecting tables on pages — the system identifies table position, cell boundaries, and extracts content in structured format. Instead of plain text, I get JSON or CSV with rows and columns, enabling programmatic table data analysis. In practice: an invoice with item lists transforms from image into structured rows (product, quantity, price, total).

Signature and Digital Mark Detection

The system automatically detects signature areas in documents — critical for authenticity verification. I use image classification models to distinguish signatures from regular text. The system remembers signature positions, enabling later signature comparison across documents (e.g., verify the same person signed). The system also detects seals, stamps, and other digital marks.

Document Structure and Hierarchy Extraction

I apply document structure analysis algorithms to identify headings, subsections, paragraphs, lists, and other elements. The system creates hierarchical document structure (document tree) clarifying content organization. Based on this, I generate automatic table of contents with page numbers. This is especially useful for large documents (technical documentation, annual reports).

RAG Indexing in Qdrant

After text extraction and structuring, all document information is converted to vector representations (embeddings) and loaded into Qdrant — a specialized vector database. I split documents into semantic blocks (paragraphs, sections) and index each separately. Metadata includes: page number, section number, element type (heading, table, text, signature).

This enables semantic search: "find all delivery timeline mentions in the contract" — the system finds matching paragraphs even if exact phrasing differs, but meaning matches.

Fast Search and Relevant Fragment Highlighting

Thanks to Qdrant, document search executes in milliseconds. Users can query in natural language ("what penalties for late payment are provided?"), the system finds the relevant paragraph and highlights it. Results return not just text but exact document position (page, coordinates), enabling users to quickly locate information in the original file.

Document Analysis via LLM

I use LLM (GPT-4 or local models) combined with RAG for deep document analysis. The system can automatically: extract key contract conditions, identify potential risks, compare contracts finding differences, answer content questions. All Qdrant information is sent to LLM with context, enabling models to provide accurate, well-reasoned answers.

Example: "What happens if I don't pay the invoice on time?" — the system finds penalties, interest rates, and conditions, formulating a clear user-friendly answer.

Processing Scans and Multipage Documents

The system handles documents of any quality — from 50-year-old archive scans to modern PDFs. DeepSeek OCR manages various scan quality levels. For multipage documents (100+ page reports), I use parallel processing — each page processes separately, results combine. In practice: 100-page reports process in 2-3 minutes.

Preserving Original Formatting and Layout

I apply layout-aware OCR — the system attempts preserving original formatting: element positions, text grouping, table positions. This enables later visual document display (e.g., web interface) maintaining original layout, simplifying user navigation.

Integration and Workflow

The entire process is organized in N8N workflow: document upload → preprocessing → OCR → structuring → table and signature analysis → Qdrant indexing → user notification. Users upload documents via web interface (Vue.js + FastAPI), and the system automatically processes them, after which they're ready for search and analysis.

Architecture scales: I can process multiple documents simultaneously through task queues, handling large volumes without performance degradation.

Export and Integration

The system can export extracted data in various formats: JSON (for programmatic processing), Markdown (for reading), CSV (for tables), Word documents (preserving formatting). Integration with document management systems (DMS), CRM, ERP, and other enterprise systems is possible via API.

Important Organizational Considerations

First — document confidentiality and security. Contracts, invoices, and business documents often contain sensitive information. Using local DeepSeek OCR is critical — data doesn't reach the cloud, everything processes on your protected server. I also implement access control: only authorized users see analysis results.

Second — OCR quality verification. Even best OCR models aren't perfect, especially for complex documents or historical scans. I recommend establishing a verification process: the system marks low-confidence areas (confidence score < 80%), and operators can manually check them. In practice, this adds minor time overhead but guarantees accuracy.

Third — large document management. For very large files (1000+ page technical documentation), the system must work efficiently. I use chunking — splitting into parts, parallel processing, and asynchronous operations. This gets users results quickly, even for large documents.

Fourth — supporting various formats. Documents arrive in different formats: PDF (digital and scanned), images (JPG, PNG, TIFF), Word, Excel. The system must support all formats. I use specialized libraries for each (PyPDF2 for PDF, python-docx for Word, etc.).

Fifth — updates and improvements. After initial deployment, the system should improve: I collect metrics (OCR accuracy, processing time, user feedback) and periodically update models and algorithms based on this. In practice: after several months of operation, accuracy increases thanks to fine-tuning on real company data.

Sixth — documentation creation and maintenance. Users need understanding: how the system works, which formats are supported, what limitations exist. I create detailed documentation with usage examples, best practices, and FAQ.