Image-to-Text
Transformers
Safetensors
mistral3
text-generation
ocr
document-understanding
vision-language
pdf
tables
forms
Eval Results
๐ช๐บ Region: EU
Instructions to use lightonai/LightOnOCR-1B-1025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lightonai/LightOnOCR-1B-1025 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="lightonai/LightOnOCR-1B-1025")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("lightonai/LightOnOCR-1B-1025") model = AutoModelForSeq2SeqLM.from_pretrained("lightonai/LightOnOCR-1B-1025", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Comprehensive testing video
#8
by ritheshSree - opened
Congratulations on a powerful OCR Model
I did some comprehensive testing and here is the video: https://youtu.be/nHIaLWHtcag
I did find some issues with handwritten data
Hi,
Thank you for the thorough testing!
I would like to point out that the current version of the model is primarily trained on PDF data, which means handwritten data and screenshots are out of distribution. However, the model still performs reasonably well on these out-of-distribution tasks. We have also shared a finetuning notebook where the model is finetuned on handwritten data. You can try it out here.