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
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
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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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
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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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