paudelanil/trocr-devanagari

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Model Details

Model Description

The trocr-devnagari model leverages the TrOCR architecture, utilizing Google ViT as the encoder and NepBERT as the decoder. This model is designed for handwriting recognition (HTR) of Nepali words, focusing on converting handwritten text into machine-readable text. The model is optimized specifically for word-level text detection, making it suitable for applications in digitizing handwritten Nepali documents.

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Uses

Direct Use

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Downstream Use [optional]

This model is primarily intended for: - Handwritten document transcription - Digitization of Nepali handwritten text - Enhancing accessibility for Nepali language users.

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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

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Training Details

Training Data

• Dataset: The model was trained on the IIIT-HW dataset, which contains a diverse set of handwritten text samples in Nepali.
• Preprocessing: Specific preprocessing steps were applied to enhance the model's performance, including data augmentation techniques suitable for handwritten text.

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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