Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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