---
library_name: transformers
tags: ["ocr", "handwritten-text-recognition", "vision-encoder-decoder", "trocr", "image-to-text"]
---

# TrOCR - Handwritten Text Recognition Model

A fine-tuned TrOCR (Transformer OCR) model for handwritten text recognition, built on the vision-encoder-decoder architecture. This model can transcribe handwritten text from images into machine-readable text.

## Model Details

### Model Description

This is a TrOCR model that combines a Vision Transformer (ViT) encoder with a Transformer decoder to perform handwritten text recognition. The model has been trained to convert handwritten text images into text output.

- **Developed by:** Fine-tuned from Microsoft's TrOCR architecture
- **Model type:** Vision-Encoder-Decoder (TrOCR)
- **Language(s):** Multi-language support (based on training data)
- **License:** [Please specify your license]
- **Finetuned from model:** Microsoft's TrOCR base model

### Model Architecture

- **Encoder:** Vision Transformer (ViT) with 12 layers, 12 attention heads, 768 hidden size
- **Decoder:** Transformer decoder with 12 layers, 16 attention heads, 1024 hidden size
- **Image input:** 384x384 pixels, 3 channels (RGB)
- **Vocabulary size:** 50,265 tokens
- **Max sequence length:** 512 tokens

## Uses

### Direct Use

This model is designed for:
- **Handwritten text recognition** from images
- **Document digitization** and transcription
- **Historical document analysis**
- **Form processing** and data extraction
- **Educational applications** (grading handwritten assignments)

### Downstream Use

The model can be fine-tuned for:
- **Specific handwriting styles** or languages
- **Domain-specific documents** (medical, legal, academic)
- **Real-time OCR applications**
- **Mobile OCR apps**

### Out-of-Scope Use

- **Printed text recognition** (use standard OCR tools instead)
- **Handwriting style analysis** or personality assessment
- **Text generation** (this is a recognition model, not generative)
- **Low-quality or extremely blurry images**

## Bias, Risks, and Limitations

### Limitations

- **Image quality dependency:** Performance degrades with poor image quality
- **Handwriting style variation:** May struggle with unusual or artistic handwriting
- **Language bias:** Performance depends on training data language distribution
- **Context sensitivity:** May misinterpret text without proper context

### Recommendations

- Ensure input images are clear and well-lit
- Use appropriate image preprocessing for optimal results
- Validate outputs for critical applications
- Consider domain-specific fine-tuning for specialized use cases

## How to Get Started with the Model

### Basic Usage

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

# Load model and processor
processor = TrOCRProcessor.from_pretrained("your-model-path")
model = VisionEncoderDecoderModel.from_pretrained("your-model-path")

# Load and process image
image = Image.open("handwritten_text.jpg").convert("RGB")

# Generate text
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(f"Recognized text: {generated_text}")
```

### Requirements

```bash
pip install transformers torch pillow
```

## Training Details

### Training Data

[Specify your training dataset details here]

### Training Procedure

#### Preprocessing
- Images resized to 384x384 pixels
- Normalized with mean [0.5, 0.5, 0.5] and std [0.5, 0.5, 0.5]
- RGB conversion and rescaling applied

#### Training Hyperparameters
- **Training regime:** [Specify training precision and regime]
- **Image size:** 384x384
- **Max sequence length:** 512 tokens

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data
[Specify your evaluation dataset]

#### Factors
- Image quality and resolution
- Handwriting style and legibility
- Text length and complexity
- Language and script type

#### Metrics
- **Character Error Rate (CER)**
- **Word Error Rate (WER)**
- **Accuracy at character/word level**

### Results

[Include your model's performance metrics here]

## Technical Specifications

### Model Architecture and Objective

The model uses a **Vision-Encoder-Decoder** architecture:
- **Encoder:** ViT processes image patches to extract visual features
- **Decoder:** Transformer decoder generates text tokens autoregressively
- **Objective:** Minimize cross-entropy loss between predicted and ground truth text

### Compute Infrastructure

#### Hardware
[Specify training hardware]

#### Software
- **Transformers version:** 4.55.1
- **PyTorch compatibility:** [Specify version]
- **CUDA support:** [Specify if applicable]

## Citation

If you use this model in your research, please cite:

**BibTeX:**
```bibtex
@misc{trocr-handwritten-recognition,
  title={TrOCR Handwritten Text Recognition Model},
  author={[Your Name/Organization]},
  year={2024},
  url={[Model URL]}
}
```

## Model Card Authors

[Your Name/Organization]

## Model Card Contact

[Your contact information]

## Acknowledgments

This model is based on the TrOCR architecture developed by Microsoft Research. Special thanks to the Hugging Face team for the transformers library and the open-source community for contributions to OCR research.