Add/update README with usage instructions

README.md

@@ -1,199 +1,184 @@
 ---
 library_name: transformers
-tags: []
+tags: ["ocr", "handwritten-text-recognition", "vision-encoder-decoder", "trocr", "image-to-text"]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# 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
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **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 Sources [optional]
+### Model Architecture
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+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 [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- **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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Limitations
 
-[More Information Needed]
+- **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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further 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
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+### Basic Usage
 
-### Training Data
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+# Load model and processor
+processor = TrOCRProcessor.from_pretrained("your-model-path")
+model = VisionEncoderDecoderModel.from_pretrained("your-model-path")
 
-[More Information Needed]
+# Load and process image
+image = Image.open("handwritten_text.jpg").convert("RGB")
 
-### Training Procedure
+# 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]
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+print(f"Recognized text: {generated_text}")
+```
 
-#### Preprocessing [optional]
+### Requirements
 
-[More Information Needed]
+```bash
+pip install transformers torch pillow
+```
 
+## Training Details
 
-#### Training Hyperparameters
+### Training Data
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+[Specify your training dataset details here]
 
-#### Speeds, Sizes, Times [optional]
+### Training Procedure
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### 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
 
-[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [Specify training precision and regime]
+- **Image size:** 384x384
+- **Max sequence length:** 512 tokens
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+[Specify your evaluation dataset]
 
 #### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- Image quality and resolution
+- Handwriting style and legibility
+- Text length and complexity
+- Language and script type
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Character Error Rate (CER)**
+- **Word Error Rate (WER)**
+- **Accuracy at character/word level**
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+[Include your model's performance metrics here]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+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
 
-[More Information Needed]
-
 #### Hardware
-
-[More Information Needed]
+[Specify training hardware]
 
 #### Software
+- **Transformers version:** 4.55.1
+- **PyTorch compatibility:** [Specify version]
+- **CUDA support:** [Specify if applicable]
 
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+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]}
+}
+```
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+## Model Card Authors
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[Your Name/Organization]
 
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+## Model Card Contact
 
-[More Information Needed]
+[Your contact information]
 
-## Model Card Contact
+## Acknowledgments
 
-[More Information Needed]
+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.