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Mizo Ocr

The first OCR model for the Mizo language, developed by MWire Labs.

TypeCLIP visionModèle de basemicrosoft/trocr-base-printedVersionmodel

Source du modèle

Type
CLIP vision
Taille du fichier
1,24 GB
Modèle de base
microsoft/trocr-base-printed
Version
vmodel
Mis à jour
26 sept. 2026
Téléchargements
4
Tâches
Légendes
Source
Hugging Face

Description de la source

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

SplitCharacter Accuracy
Validation89.61%
Test90.68%

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.

Sources

1 source
Hugging FacePrincipalVersion 6729721bea74645e62eaf3e8daed30ec92aef5c9
huggingface.co/MWirelabs/mizo-ocr/tree/6729721bea74645e62eaf3e8daed30ec92aef5c9

Vérifié 11 sept.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 1,24 GB · SHA-256 e39a6b55eb8a…9b19 · Hugging Face

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Extraits de sources

2 extraits

--- 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