legacylipi 0.8.0

Legacy Font PDF Translator - Translate PDFs with legacy Indian font encodings to English

LegacyLipi

Legacy Font PDF Translator - Translate PDF documents with legacy Indian font encodings to English.

Installation

From PyPI (Recommended)

pip install legacylipi

Or with uv:

uv tool install legacylipi

From Source

git clone https://github.com/biswasbiplob/legacylipi.git
cd legacylipi
uv sync

Frontend (for development)

cd frontend
npm install

Usage

# CLI translation
legacylipi translate input.pdf -o output.txt

# Launch React web UI (production build served by FastAPI)
legacylipi api

# Launch legacy NiceGUI web UI (deprecated)
legacylipi ui

Problem

Millions of government documents, legal papers, and archival materials in Indian regional languages (Marathi, Hindi, Tamil, etc.) were created using legacy font encoding systems (Shree-Lipi, Kruti Dev, APS, Chanakya, etc.). These fonts map Devanagari/regional script glyphs to ASCII/Latin code points, making them unreadable by standard translation tools.

Example:

• What the PDF displays: महाराष्ट्र राजभाषा अधिनियम
• What text extraction produces: ´ÖÆüÖ¸üÖ™Òü ¸üÖ•Ö³ÖÖÂÖÖ †×¬Ö×®ÖμÖ´Ö
• What Google Translate sees: Gibberish

Solution

LegacyLipi:

1. Detects the font encoding scheme used in a PDF (legacy or Unicode)
2. Converts legacy-encoded text to proper Unicode
3. Alternatively, uses OCR to extract text from scanned PDFs
4. Translates the Unicode text to the target language
5. Outputs translated text in various formats (text, markdown, PDF)

Installation

# Clone and install
git clone https://github.com/biswasbiplob/legacylipi.git
cd legacylipi
uv sync

# With all optional backends
uv sync --all-extras

OCR Support (Optional)

LegacyLipi supports multiple OCR backends:

Backend	Description	GPU Support
Tesseract	Local, free, most language packs	CPU only
Google Vision	Cloud, paid, best accuracy	N/A
EasyOCR	Local, free, good for Indian languages	CUDA, MPS (Apple Silicon)

Tesseract (default):

# Ubuntu/Debian
sudo apt-get install tesseract-ocr tesseract-ocr-mar tesseract-ocr-hin

# macOS
brew install tesseract tesseract-lang

EasyOCR with GPU (optional):

# Install with EasyOCR support
uv sync --extra easyocr

# For GPU acceleration, install PyTorch with CUDA or MPS support

Google Vision (optional):

uv sync --extra vision
# Requires GCP credentials (GOOGLE_APPLICATION_CREDENTIALS)

See docs/cli-reference.md for detailed OCR options and language codes.

Quick Start

# Basic translation
uv run legacylipi translate input.pdf -o output.txt

# Output as PDF (preserves layout)
uv run legacylipi translate input.pdf -o output.pdf --format pdf

# OCR for scanned documents
uv run legacylipi translate input.pdf --use-ocr -o output.txt

# Use local LLM (requires Ollama)
uv run legacylipi translate input.pdf --translator ollama --model llama3.2

# Detect encoding only
uv run legacylipi detect input.pdf

See docs/cli-reference.md for complete CLI documentation.

Web UI

LegacyLipi includes a modern React-based web interface backed by a FastAPI REST API.

Production (single command)

# Serves the built React frontend + API on one port
uv run legacylipi api
# or
uv run legacylipi-web

Open http://localhost:8000 in your browser.

Development (hot-reload)

# Start both FastAPI backend and Vite dev server
./scripts/dev.sh

This runs:

• Backend at http://localhost:8000 (FastAPI with auto-reload)
• Frontend at http://localhost:5173 (Vite dev server with HMR, proxies /api to backend)

Legacy NiceGUI UI (deprecated)

The original NiceGUI-based UI is still available but deprecated:

uv run legacylipi ui
# Open http://localhost:8080

Workflow Modes:

• Scanned Copy - Create image-based PDF copy (adjust DPI, color, quality)
• Convert to Unicode - OCR + Unicode conversion without translation
• Full Translation - Complete pipeline with OCR, conversion, and translation

Features:

• Drag-and-drop PDF upload
• Workflow-based UI with mode selection
• Multiple translation backends (Translate-Shell, Google, Ollama, OpenAI, etc.)
• OCR support with engine and language selection
• Structure-preserving or flowing text modes
• Real-time SSE progress streaming
• Direct download of translated files
• Responsive dark-theme design

Translation Backends

Backend	Description	Setup
trans	translate-shell CLI (recommended)	brew install translate-shell
google	Google Translate (free API)	Works out of the box
mymemory	MyMemory API (free)	Works out of the box
ollama	Local LLM via Ollama	Ollama required
openai	OpenAI GPT models	Set OPENAI_API_KEY
gcp_cloud	Google Cloud Translation	GCP project + credentials

See docs/translation-backends.md for detailed setup guides.

Supported Encodings

Encoding	Font Family	Language	Status
shree-lipi	Shree-Lipi, Shree-Dev-0714	Marathi	✅ Built-in
kruti-dev	Kruti Dev	Hindi	✅ Built-in
aps-dv	APS-DV	Hindi	🔄 Detection only
chanakya	Chanakya	Hindi	🔄 Detection only
dvb-tt	DVB-TT, DV-TTYogesh	Hindi	🔄 Detection only
walkman-chanakya	Walkman Chanakya	Hindi	🔄 Detection only
shusha	Shusha	Hindi	🔄 Detection only

CLI Commands

Command	Description
api	Launch the React web UI + FastAPI REST API
translate	Full pipeline: parse → detect → convert → translate → output
convert	Convert legacy encoding to Unicode (no translation)
extract	Extract text from PDF (OCR or font-based)
detect	Analyze PDF and report detected encoding
scan-copy	Create an image-based scanned copy of a PDF
encodings	List supported font encodings
usage	Show API usage statistics
ui	Launch legacy NiceGUI web interface (deprecated)

See docs/cli-reference.md for full command reference.

Development

See docs/development.md for setup instructions, running tests, project structure, and adding new encodings.

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                              LegacyLipi                                 │
├─────────────────────┬───────────────────────────────────────────────────┤
│   React Frontend    │                  FastAPI Backend                   │
│  (Vite + TS + TW)   │                                                   │
│                     │   ┌──────────────────────────────────────────┐    │
│  FileUploader       │   │              REST API                    │    │
│  WorkflowSelector   │   │  /api/v1/config/*     GET config         │    │
│  Settings panels    │◄─▶│  /api/v1/sessions/*   Upload/delete      │    │
│  StatusPanel (SSE)  │   │  /api/v1/sessions/*/  Start pipeline     │    │
│  DownloadButton     │   │  /api/v1/sessions/*/progress  SSE stream │    │
│                     │   │  /api/v1/sessions/*/download  Get result │    │
│                     │   └────────────────────┬─────────────────────┘    │
├─────────────────────┘                        │                          │
│                                              ▼                          │
│  ┌──────────────────────────────────────────────────────────────────┐   │
│  │                      Core Pipeline                               │   │
│  │                                                                  │   │
│  │  PDF Parser / OCR Parser                                         │   │
│  │       │                                                          │   │
│  │  Encoding Detector → Unicode Converter                           │   │
│  │       │                                                          │   │
│  │  Translation Engine (trans, Google, Ollama, OpenAI, GCP, ...)    │   │
│  │       │                                                          │   │
│  │  Output Generator (.txt, .md, .pdf)                              │   │
│  └──────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────┘

Pipeline Flow:

1. Parse PDF → Extract text with PDF parser or OCR
2. Detect Encoding → Identify legacy encoding scheme
3. Convert to Unicode → Transform legacy text to Unicode
4. Translate → Use translation backend
5. Generate Output → Create PDF/text/markdown

License

MIT

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (uv run pytest)
5. Commit and push
6. Open a Pull Request