metadata

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Historical document analysis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

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

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:

@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.