OCR Project — Production-Oriented Development Plan

Project Plan

Build a modular, production-ready Optical Character Recognition (OCR) system structured in two phases:

• Phase 1 → Stable, usable MVP
• Phase 2 → Optimized, scalable, production-grade system

This project evolves from a functional prototype to a high-performance document intelligence service.

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Phase 1 — MVP (Functional, Stable, Demonstrable)

Deliver a working OCR API that:

• Accepts image input (printed or handwritten)
• Extracts text reliably
• Returns structured JSON output
• Optionally exports extracted text into a document

No over-optimization. No premature scaling.

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Phase 1 Architecture

Core Pipeline

Image Upload ↓ Basic Preprocessing (RGB conversion) ↓ TrOCR Model (Printed / Handwritten) ↓ Extracted Text ↓ Return JSON Response

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Design Principles - (Phase 1)

To keep the MVP clean and stable:

• ❌ No Tesseract segmentation
• ❌ No multi-stage detection logic
• ❌ No fallback heuristics
• ❌ No complex layout handling
• ✅ Single responsibility: text recognition

The goal is reliability, not sophistication.

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API Specification

Endpoint: /ocr/extract

Method: POST
Input:

• Image file (.png, .jpg, .jpeg)
• Model selection (printed or handwritten)

Response Format:

{
  "extracted_text": "Recognized text from image",
  "model_used": "printed"
}

Phase 2 — Optimization & Production Architecture

Phase 2 introduces:

Image preprocessing (deskew, contrast normalization)

Deep-learning text detection (CRAFT / DBNet)

Batch inference optimization

Model acceleration (FP16 / ONNX)

Confidence scoring

Layout awareness (tables, forms)

Scalable deployment (Docker + queue workers)

Tech Stack (Phase 1)

Python
FastAPI
PyTorch
HuggingFace Transformers
TrOCR (Printed + Handwritten)
Uvicorn