---
library_name: transformers
language:
- ur
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->


This is an Urdu OCR model designed for handwriting recognition tasks. It utilizes a VisionEncoderDecoderModel with a ViT-based encoder and a BERT-based decoder, fine-tuned on a custom dataset for robust and accurate text extraction from images.



## 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:** Fajar Pervaiz
- **Model type:** VisionEncoderDecoderModel
- **Language(s) (NLP):** Urdu (ur)
- **Finetuned from model [optional]:** facebook/deit-base-distilled-patch16-384, bert-base-multilingual-cased

### 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. -->
This model can be directly used for Urdu handwriting recognition tasks, particularly for extracting text from scanned documents or handwritten notes.


### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
This model can be fine-tuned further for specific handwriting datasets or integrated into larger OCR systems for Urdu or multilingual text recognition.



### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
The model is not suitable for languages other than Urdu or domains with highly noisy or distorted images without further fine-tuning.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model may exhibit biases inherent in the training data. Misrecognition of complex or ambiguous handwriting is possible. Users should carefully evaluate its performance in their specific use case.



### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should test the model thoroughly on their specific dataset and consider additional fine-tuning if required. Misuse in sensitive applications (e.g., legal or medical document OCR) should be avoided without rigorous evaluation.
## How to Get Started with the Model

Use the code below to get started with the model.

from transformers import VisionEncoderDecoderModel, TrOCRProcessor
processor = TrOCRProcessor.from_pretrained("path/to/processor")
model = VisionEncoderDecoderModel.from_pretrained("path/to/model")




## 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. -->
The training data comprises 46,742 image-text pairs from a custom dataset of Urdu handwritten texts.



### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Images were resized to 384x384 pixels and normalized. Augmentations such as Elastic Transform and Gaussian Blur were applied to enhance robustness.

#### Preprocessing [optional]




#### Training Hyperparameters

- **Training regime:**  <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
- Training regime: Mixed precision (fp16)
- Learning rate: 4e-5
- Batch size: 8
- Epochs: 12
- Optimizer: AdamW
- Scheduler: Linear decay

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->


## 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. -->
A subset of 4,675 image-text pairs was used for evaluation.



#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
The model was tested on handwritten text images with varying font styles and complexities.





#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->



### Results



#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->



## 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:** NVIDIA GPU
- **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

The model uses a VisionEncoderDecoder architecture combining a ViT encoder and a BERT decoder.


### Compute Infrastructure



#### Hardware

NVIDIA GPU (e.g., A100)


#### Software


Python, PyTorch, Hugging Face Transformers



## 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:**



## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
CER: Character Error Rate
WER: Word Error Rate
OCR: Optical Character Recognition



## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]


Fajar Pervaiz

## Model Card Contact


pervaizfajar@gmail.com