paudelanil
/

trocr-devanagari-2

@@ -11,189 +11,137 @@ tags: []
 ## 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.
-- **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]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## 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]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### 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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## 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]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### 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]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ## Model Details
+# TrOCR Devanagari - Handwritten Text Recognition
+## Overview
+TrOCR Devanagari is an end-to-end Vision Encoder-Decoder model built to recognize and convert handwritten Devanagari script (specifically for Nepali language) into machine-readable text. It leverages a Vision Transformer (ViT) as the encoder and uses a transformer-based decoder (NepBERT) to produce textual output. This project aims to assist in digitizing handwritten Nepali documents.
+## Model Architecture
+The model pipeline includes the following steps:
+1. **Text Detection:** Extracts regions of interest from scanned handwritten documents.
+2. **Image Preprocessing:** Resizes and pads input images to feed into the model.
+3. **Text Recognition:** Uses the TrOCR-based Vision Encoder Decoder model to predict handwritten text.
+4. **UI Interface (Optional):** Displays the results and enables user interaction with the system.
+## Model Information
+- **Model Name:** TrOCR Devanagari
+- **Developed by:** Anil Paudel, Aayush Puri, Yubaraj Sigdel
+- **Language:** Nepali
+- **License:** MIT (tentative)
+- **Model Type:** Vision Encoder Decoder
+- **Repository:** [paudelanil/trocr-devanagari-2](https://huggingface.co/paudelanil/trocr-devanagari-2)
+- **Training Data:** IIIT-HW Dataset
+- **Evaluation Metric:** CER (Character Error Rate)
+- **Hardware Used:** NVIDIA RTX A4500
+## Getting Started
+### Installation
+To use the model, ensure you have the following Python packages installed:
+```bash
+pip install torch transformers pillow
+```
+### Preprocessing Function
+The image preprocessing function is used to resize images to the target size while maintaining the aspect ratio and padding the remaining space.
+```python
+from PIL import Image
+def preprocess_image(image):
+    target_size = (224, 224)
+    original_size = image.size
+    aspect_ratio = original_size[0] / original_size[1]
+    if aspect_ratio > 1:
+        new_width = target_size[0]
+        new_height = int(target_size[0] / aspect_ratio)
+    else:
+        new_height = target_size[1]
+        new_width = int(target_size[1] * aspect_ratio)
+    resized_img = image.resize((new_width, new_height))
+    padding_width = target_size[0] - new_width
+    padding_height = target_size[1] - new_height
+    pad_left = padding_width // 2
+    pad_top = padding_height // 2
+    pad_image = Image.new('RGB', target_size, (255, 255, 255))
+    pad_image.paste(resized_img, (pad_left, pad_top))
+    return pad_image
+```
+### Prediction Code
+Here’s how you can use the model for text recognition:
+```python
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, VisionEncoderDecoderModel, ViTFeatureExtractor, TrOCRProcessor
+# Load the model and processor
+tokenizer = AutoTokenizer.from_pretrained("aayushpuri01/TrOCR-Devanagari")
+model1 = VisionEncoderDecoderModel.from_pretrained("aayushpuri01/TrOCR-Devanagari")
+feature_extractor1 = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
+processor1 = TrOCRProcessor(feature_extractor=feature_extractor1, tokenizer=tokenizer)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model1.to(device)
+# Prediction function
+def predict(image):
+    # Preprocess the image
+    image = Image.open(image).convert("RGB")
+    image = preprocess_image(image)
+    pixel_values = processor1(image, return_tensors="pt").pixel_values.to(device)
+    # Generate text from the image
+    generated_ids = model1.generate(pixel_values)
+    generated_text = processor1.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_text
+```
+### Usage Example
+```python
+# Load and predict
+image_path = "path_to_your_image.jpg"
+predicted_text = predict(image_path)
+print("Predicted Text:", predicted_text)
+```
+## Training Hyperparameters
+```python
+training_args = Seq2SeqTrainingArguments(
+    predict_with_generate=True,
+    evaluation_strategy="steps",
+    per_device_train_batch_size=32,
+    per_device_eval_batch_size=32,
+    output_dir='/workspace/checkpoint-save/',
+    save_total_limit=2,
+    logging_steps=2,
+    save_steps=1000,
+    eval_steps=1000,
+    save_strategy="steps",
+    load_best_model_at_end=True,
+    metric_for_best_model="cer",
+    greater_is_better=False,
+    num_train_epochs=15
+)
+```
+## License
+The model is shared under the MIT license. For details, see the [LICENSE](LICENSE) file.
+## Acknowledgments
+This model is based on the 🤗 Transformers library, and uses the ViT encoder and NepBERT decoder architecture. Special thanks to the IIIT-HW dataset contributors.
+---
+Feel free to explore the project and contribute to the repository!