High-performance German document OCR - Local & Cloud
Features
| Feature | Local | Cloud |
|---|---|---|
| German Documents | Invoices, contracts, forms | All document types |
| Output Formats | Markdown, JSON, text | JSON, Markdown, text, n8n |
| PDF Support | Images only | Up to 50 pages |
| Privacy | 100% local | DSGVO-konform (Frankfurt) |
| Speed | ~5s/page | ~2-3s/page |
| Backends | Ollama, llama.cpp, HuggingFace | Cloud API |
| Hardware | CPU, GPU, NPU (CUDA/Metal/Vulkan/OpenVINO) | Managed |
Installation
Python
pip install german-ocr
Node.js
npm install german-ocr
PHP
composer require keyvan/german-ocr
Quick Start
Option 1: Cloud API (Recommended)
No GPU required. Get your API credentials at app.german-ocr.de
from german_ocr import CloudClient
# API Key + Secret (Secret is only shown once at creation!)
client = CloudClient(
api_key="gocr_xxxxxxxx",
api_secret="your_64_char_secret_here"
)
# Simple extraction
result = client.analyze("invoice.pdf")
print(result.text)
# Structured JSON output
result = client.analyze(
"invoice.pdf",
prompt="Extrahiere Rechnungsnummer und Gesamtbetrag",
output_format="json"
)
print(result.text)
Node.js
const { GermanOCR } = require('german-ocr');
const client = new GermanOCR(
process.env.GERMAN_OCR_API_KEY,
process.env.GERMAN_OCR_API_SECRET
);
const result = await client.analyze('invoice.pdf', {
model: 'german-ocr-ultra'
});
console.log(result.text);
PHP
<?php
use GermanOCR\GermanOCR;
$client = new GermanOCR(
getenv('GERMAN_OCR_API_KEY'),
getenv('GERMAN_OCR_API_SECRET')
);
$result = $client->analyze('invoice.pdf', [
'model' => GermanOCR::MODEL_ULTRA
]);
echo $result['text'];
Option 2: Local (Ollama)
Requires Ollama installed.
# Install model
ollama pull Keyvan/german-ocr-turbo
from german_ocr import GermanOCR
ocr = GermanOCR()
text = ocr.extract("invoice.png")
print(text)
Option 3: Local (llama.cpp)
For maximum control and edge deployment with GGUF models.
# Install with GPU support (CUDA)
CMAKE_ARGS="-DGGML_CUDA=on" pip install german-ocr[llamacpp]
# Or CPU only
pip install german-ocr[llamacpp]
from german_ocr import GermanOCR
# Auto-detect best device (GPU/CPU)
ocr = GermanOCR(backend="llamacpp")
text = ocr.extract("invoice.png")
# Force CPU only
ocr = GermanOCR(backend="llamacpp", n_gpu_layers=0)
# Full GPU acceleration
ocr = GermanOCR(backend="llamacpp", n_gpu_layers=-1)
Cloud Models
| Model | Parameter | Best For |
|---|---|---|
| German-OCR Ultra | german-ocr-ultra |
Maximale Präzision, Strukturerkennung |
| German-OCR Pro | german-ocr-pro |
Balance aus Speed & Qualität |
| German-OCR Turbo | german-ocr |
DSGVO-konform, lokale Verarbeitung in DE |
Model Selection
from german_ocr import CloudClient
client = CloudClient(
api_key="gocr_xxxxxxxx",
api_secret="your_64_char_secret_here"
)
# German-OCR Ultra - Maximale Präzision
result = client.analyze("dokument.pdf", model="german-ocr-ultra")
# German-OCR Pro - Schnelle Cloud (Standard)
result = client.analyze("dokument.pdf", model="german-ocr-pro")
# German-OCR Turbo - Lokal, DSGVO-konform
result = client.analyze("dokument.pdf", model="german-ocr")
CLI Usage
Cloud
# Set API credentials (Secret shown only once at creation!)
export GERMAN_OCR_API_KEY="gocr_xxxxxxxx"
export GERMAN_OCR_API_SECRET="your_64_char_secret_here"
# Extract text (uses German-OCR Pro by default)
german-ocr --cloud invoice.pdf
# Use German-OCR Turbo (DSGVO-konform, lokal)
german-ocr --cloud --model german-ocr invoice.pdf
# JSON output with German-OCR Ultra
german-ocr --cloud --model german-ocr-ultra --output-format json invoice.pdf
# With custom prompt
german-ocr --cloud --prompt "Extrahiere alle Betraege" invoice.pdf
Local
# Single image
german-ocr invoice.png
# Batch processing
german-ocr --batch ./invoices/
# JSON output
german-ocr --format json invoice.png
Cloud API
Output Formats
| Format | Description |
|---|---|
text |
Plain text (default) |
json |
Structured JSON |
markdown |
Formatted Markdown |
n8n |
n8n-compatible format |
Progress Tracking
from german_ocr import CloudClient
client = CloudClient(
api_key="gocr_xxxxxxxx",
api_secret="your_64_char_secret"
)
def on_progress(status):
print(f"Page {status.current_page}/{status.total_pages}")
result = client.analyze(
"large_document.pdf",
on_progress=on_progress
)
Async Processing
# Submit job with German-OCR Pro
job = client.submit("document.pdf", model="german-ocr-pro", output_format="json")
print(f"Job ID: {job.job_id}")
# Check status
status = client.get_job(job.job_id)
print(f"Status: {status.status}")
# Wait for result
result = client.wait_for_result(job.job_id)
# Cancel job
client.cancel_job(job.job_id)
Account Info
# Check balance
balance = client.get_balance()
print(f"Balance: {balance}")
# Usage statistics
usage = client.get_usage()
print(f"Usage: {usage}")
Local Models
Ollama Models
| Model | Size | Speed | Best For |
|---|---|---|---|
| german-ocr-turbo | 1.9 GB | ~5s | Recommended |
| german-ocr | 3.2 GB | ~7s | Standard |
GGUF Models (llama.cpp)
| Model | Size | Speed | Best For |
|---|---|---|---|
| german-ocr-2b | 1.5 GB | ~5s (GPU) / ~25s (CPU) | Edge/Embedded |
| german-ocr-turbo | 1.9 GB | ~5s (GPU) / ~20s (CPU) | Best accuracy |
Hardware Support:
- CUDA (NVIDIA GPUs)
- Metal (Apple Silicon)
- Vulkan (AMD/Intel/NVIDIA)
- OpenVINO (Intel NPU)
- CPU (all platforms)
Pricing
See current pricing at app.german-ocr.de
License
Apache 2.0 - See LICENSE for details.
Author
Keyvan Hardani - keyvan.ai
Made with love in Germany
Metadata
Release files for german-ocr 0.6.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| german_ocr-0.6.0.tar.gz | 31.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| german_ocr-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 63.4 kB
Release files / german_ocr-0.6.0.tar.gz
| Download URL | german_ocr-0.6.0.tar.gz |
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| Size | 31.7 kB |
| Tags | Source |
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| Uploaded via |
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