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README.md

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-library_name: transformers
-language:
-- ur
-pipeline_tag: image-to-text
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-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-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.
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **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
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- 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.
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-### Downstream Use [optional]
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-<!-- 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.
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-### Out-of-Scope Use
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-<!-- 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.
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-## Bias, Risks, and Limitations
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-<!-- 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.
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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
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-Use the code below to get started with the model.
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-from transformers import VisionEncoderDecoderModel, TrOCRProcessor
-processor = TrOCRProcessor.from_pretrained("path/to/processor")
-model = VisionEncoderDecoderModel.from_pretrained("path/to/model")
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-## Training Details
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-### Training Data
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-<!-- 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.
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-### Training Procedure
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-<!-- 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.
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **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
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
-A subset of 4,675 image-text pairs was used for evaluation.
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-#### Factors
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-<!-- 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.
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **Hardware Type:** NVIDIA GPU
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-The model uses a VisionEncoderDecoder architecture combining a ViT encoder and a BERT decoder.
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-### Compute Infrastructure
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-#### Hardware
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-NVIDIA GPU (e.g., A100)
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-#### Software
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-Python, PyTorch, Hugging Face Transformers
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-## Glossary [optional]
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-<!-- 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
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-Fajar Pervaiz
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-## Model Card Contact
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-[email protected]
+This model, `cxfajar197/urdu-ocr`, is trained on Urdu data specifically designed for OCR tasks. It works best with single-line Urdu text images, primarily focusing on printed text. The model is optimized for extracting accurate Urdu text from such images and can be easily utilized using the Hugging Face pipeline API.