---
library_name: transformers
tags:
- htr
- nepali htr
- devnagari htr
license: mit
datasets:
- c3rl/IIIT-INDIC-HW-WORDS-Hindi
language:
- ne
metrics:
- cer
base_model:
- google/vit-base-patch16-224-in21k
- amitness/roberta-base-ne
pipeline_tag: image-to-text
---

# Model Card for Model ID

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## Model Details

### Model Description

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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

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### Direct Use

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### Downstream Use [optional]

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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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## How to Get Started with the Model

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## Training Details

### Training Data

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- **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

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#### Summary



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## Environmental Impact

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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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