Mizo Ocr
The first OCR model for the Mizo language, developed by MWire Labs.
Fuente del modelo
- Tipo
- CLIP vision
- Tamaño del archivo
- 1,24 GB
- Modelo base
- microsoft/trocr-base-printed
- Versión
- vmodel
- Actualizado
- 26 sept 2026
- Descargas
- 4
- Tareas
- Descripción
- Fuente
- Hugging Face
Descripción de la fuente
MizoOCR
The first OCR model for the Mizo language, developed by MWire Labs.
Model Description
MizoOCR is a fine-tuned TrOCR model for recognizing printed Mizo text, including its unique diacritical characters (â, ê, î, ô, û). It is built on microsoft/trocr-base-printed and trained on 70,000 deduplicated mix of curated + synthetic image-text pairs drawn from a 200k dataset generated by MWire Labs.
Performance
| Split | Character Accuracy |
|---|---|
| Validation | 89.61% |
| Test | 90.68% |
Training Data
- Total unique samples after deduplication: 102,171
- Training samples: 70,000
- Validation samples: 5,000
- Test samples: 5,000
Usage
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
processor = TrOCRProcessor.from_pretrained("MWirelabs/mizo-ocr")
model = VisionEncoderDecoderModel.from_pretrained("MWirelabs/mizo-ocr")
image = Image.open("mizo_text.jpg").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values
generated = model.generate(pixel_values)
text = processor.tokenizer.decode(generated[0], skip_special_tokens=True)
print(text)
Limitations
- Trained primarily on synthetic data with a small curated dataset; accuracy on real scanned documents may vary
- Optimized for printed text, not handwritten
- Performance may vary on heavily degraded or low-quality images
Citation
If you use this model, please cite:
@misc{mwirelabs2026mizoocr,
title={MizoOCR: First OCR Model for the Mizo Language},
author={MWire Labs},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/MWirelabs/mizo-ocr}
}
About MWire Labs
MWire Labs is an AI research organization based in Shillong, Meghalaya, India, specializing in language technology for Northeast India's indigenous languages.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 11 sept
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: cc-by-4.0 language: - lus tags: - ocr - mizo - northeast-india - trocr - image-to-text - low-resource model_name: mizo-ocr base_model: microsoft/trocr-base-printed --- # MizoOCR The first OCR model for the Mizo language, developed by [MWire Labs](https://huggingface.co/MWirelabs). ## Model Description MizoOCR is a fine-tuned TrOCR model for recognizing printed Mizo text, including its unique diacritical characters (â, ê, î, ô, û). It is built on `microsoft/trocr-base-printed` and trained on 70,000 deduplicated mix of curated + synthetic image-text pairs drawn from a 200k dataset generated by MWire Labs. ## Performance | Split | Character Accuracy | |-------|-------------------| | Validation | 89.61% | | Test | 90.68% | ## Training Data - **Total unique samples after deduplication:** 102,171 - **Training samples:** 70,000 - **Validation samples:** 5,000 - **Test samples:** 5,000 ## Usage ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image processor = TrOCRProcessor.from_pretrained("MWirelabs/mizo-ocr") model = VisionEncoderDecoderModel.from_pretrained("MWirelabs/mizo-ocr") image = Image.open("mizo_text.jpg").convert("RGB") pixel_values = processor(image, return_tensors="pt").pixel_values generated = model.generate(pixel_values) text = processor.tokenizer.decode(generated[0], skip_special_tokens=True) print(text) ``` ## Limitations - Trained primarily on synthetic data with a small curated dataset; accuracy on real scanned documents may vary - Optimized for printed text, not handwritten - Performance may vary on heavily degraded or low-quality images ## Citation If you use this model, please cite: ``` @misc{mwirelabs2026mizoocr, title={MizoOCR: First OCR Model for the Mizo Language}, author={MWire Labs}, year={2026}, publisher={Hugging Face}, url={https://huggingface.co/MWirelabs/mizo-ocr} } ``` ## About MWire Labs MWire Labs is an AI research organization based in Shillong, Meghalaya, India, specializing in language technology for Northeast India's indigenous languages.
Source context: 4 downloads · 0 likes · Pipeline image-to-text · Repo MWirelabs/mizo-ocr
