---
license: apache-2.0
metrics:
- cer
pipeline_tag: image-to-text
---
# Model description

**Model Name:** cyrillic-htr-model

**Model Type:** Transformer-based OCR (TrOCR)

**Base Model:** microsoft/trocr-large-handwritten

**Purpose:** Handwritten text recognition

**Languages:** Cyrillic

**License:** Apache 2.0

This model is a fine-tuned version of the microsoft/trocr-large-handwritten model, specialized for recognizing handwritten cyrillic text. At the moment it has been trained on the dataset (number of pages 740) from 17th to 20th centuries.

# Model Architecture

The model is based on a Transformer architecture (TrOCR) with an encoder-decoder setup:

- The encoder processes images of handwritten text.
- The decoder generates corresponding text output.

# Intended Use

This model is designed for handwritten text recognition and is intended for use in:

- Document digitization (e.g., archival work, historical manuscripts)
- Handwritten notes transcription

# Training data

The training dataset includes more than 30000 samples of handwritten text rows.

# Evaluation

The model was evaluated on test dataset. Below are key metrics:

**Character Error Rate (CER):** 8

**Test Dataset Description:** size ~33 400 text rows

# How to Use the Model

You can use the model directly with Hugging Face’s pipeline function or by manually loading the processor and model.

```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

# Load the model and processor
processor = TrOCRProcessor.from_pretrained("Kansallisarkisto/cyrillic-htr-model/processor")
model = VisionEncoderDecoderModel.from_pretrained("Kansallisarkisto/cyrillic-htr-model")

# Open an image of handwritten text
image = Image.open("path_to_image.png")

# Preprocess and predict
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)

```

# Limitations and Biases

The model was trained primarily on handwritten text that uses basic Cyrillic characters.

# Future Work

Potential improvements for this model include:

- Expanding training data: Incorporating more diverse handwriting styles and languages.
- Optimizing for specific domains: Fine-tuning the model on domain-specific handwriting.

# Citation

If you use this model in your work, please cite it as:

@misc{cyrillic_htr_model_2025,

  author = {Kansallisarkisto},

  title = {Cyrillic HTR Model: Handwritten Text Recognition},
  
  year = {2025},
  
  publisher = {Hugging Face},
  
  howpublished = {\url{https://huggingface.co/Kansallisarkisto/cyrillic-htr-model/}},

}

## Model Card Authors

Author: Kansallisarkisto