Skip to main content

​
Layered Architecture

The Unified OCR system is built on a layered architecture that separates concerns and enables flexible engine composition.

​
Layer 0 — Document Preparation

Handles file validation, PDF-to-image conversion, and image preprocessing before OCR processing begins.
  • File validation: Checks file existence and supported formats (.png, .jpg, .jpeg, .bmp, .tiff, .tif, .webp, .pdf)
  • PDF conversion: Converts PDF pages to images at configurable DPI using convert_document
  • Image preprocessing: Optional rotation correction, contrast enhancement, and noise reduction

​
Layer 1 — OCR Engines

Each OCR engine runs independently on prepared images and returns structured OCRResult objects. Available engines:
  • EasyOCREngine — Deep learning-based, 80+ languages
  • RapidOCREngine — ONNX Runtime-based, lightweight and fast
  • TesseractOCREngine — Google’s open-source engine, 100+ languages
  • DeepSeekOCREngine — VLLM-based batch processing
  • DeepSeekOllamaOCREngine — Ollama-based local processing
  • PaddleOCREngine, PPStructureV3Engine, PPChatOCRv4Engine, PaddleOCRVLEngine — PaddlePaddle family

​
Orchestrator — OCR Class

The top-level OCR class orchestrates the full pipeline:
  1. Receives a file path
  2. Delegates to Layer 0 for document preparation
  3. Passes prepared images to the configured Layer 1 engine
  4. Aggregates results, calculates confidence scores, and tracks metrics

​
Pipeline Flow

File (PDF/Image)
    │
    ▼
Layer 0: convert_document → preprocessed images
    │
    ▼
Layer 1: Engine.process(images) → OCRResult
    │
    ▼
Orchestrator: aggregate results, metrics, confidence
    │
    ▼
Final OCRResult