This model has been fine-tuned specifically for recognizing German text in images, including handling of German-specific characters (ä, ö, ü, ß) and common German compound words.

Intended Uses

This model is designed for:

Extracting German text from scanned documents
Digitizing printed German materials
Reading German text from photographs
Processing German forms and receipts
Any German text recognition tasks

How to Use

Basic Usage

python

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests

# Load model and processor
processor = TrOCRProcessor.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
model = VisionEncoderDecoderModel.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")

# Load image
url = "path_to_your_german_text_image.jpg"
image = Image.open(url).convert("RGB")

# Process
pixel_values = processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)

Batch Processing

python

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

processor = TrOCRProcessor.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
model = VisionEncoderDecoderModel.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")

# Multiple images
images = [Image.open(f"image_{i}.jpg").convert("RGB") for i in range(5)]

# Batch process
pixel_values = processor(images, return_tensors="pt", padding=True).pixel_values
generated_ids = model.generate(pixel_values)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)

for text in generated_texts:
    print(text)

With GPU Acceleration

python

import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = TrOCRProcessor.from_pretrained("YOUR_USERNAME/deepseek-ocr-german")
model = VisionEncoderDecoderModel.from_pretrained("YOUR_USERNAME/deepseek-ocr-german").to(device)

image = Image.open("german_text.jpg").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)

generated_ids = model.generate(pixel_values)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(text)

Training Details

Training Data

The model was fine-tuned on a synthetic German OCR dataset containing 200,000 images with:

Diverse German sentences covering multiple domains (everyday conversation, news, literature, technical, business)
Various fonts and font sizes (16-48pt)
Multiple augmentations: noise, blur, brightness/contrast variations
Different text and background colors

Data Split:

Train: 180,000 samples (90%)
Validation: 10,000 samples (5%)
Test: 10,000 samples (5%)

Training Framework

python

# Example training configuration
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments

training_args = Seq2SeqTrainingArguments(
    output_dir="./deepseek-ocr-german",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    learning_rate=5e-5,
    num_train_epochs=10,
    logging_steps=100,
    save_steps=1000,
    eval_steps=1000,
    evaluation_strategy="steps",
    save_total_limit=2,
    fp16=True,
    predict_with_generate=True,
)

# Limitations

Font coverage: Performance may vary with handwritten text
Image quality: Works best with clear, high-contrast images
Domain specificity: Best performance on printed German text similar to training distribution

Citation

If you use this model, please cite:

bibtex

@misc{deepseek-ocr-german,
  author = {Santosh Pandit},
  title = {DeepSeek OCR - German Fine-tuned},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/deepseek-ocr-german}},
}

Model Card Contact

For questions or feedback, please open an issue on the model repository or contact [hello@neuralabs.one].

Acknowledgments

Base model: DeepSeek AI
Training data generation: LM Studio with local LLM
Framework: Hugging Face Transformers

Uploaded finetuned model

Developed by: neuralabs
License: apache-2.0
Finetuned from model : deepseek-ai/DeepSeek-OCR

neuralabs/deepseek_ocr_de

DeepSeek OCR - Fine-tuned for German/Deutsch

Model Description

Intended Uses

How to Use

Basic Usage

Batch Processing

With GPU Acceleration

Training Details

Training Data

Training Framework

# Limitations

Citation

Model Card Contact

Acknowledgments

Uploaded finetuned model

neuralabs/
deepseek_ocr_de