Instructions to use kkatiz/thai-trocr-thaigov-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kkatiz/thai-trocr-thaigov-v2 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="kkatiz/thai-trocr-thaigov-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("kkatiz/thai-trocr-thaigov-v2") model = AutoModelForImageTextToText.from_pretrained("kkatiz/thai-trocr-thaigov-v2") - Notebooks
- Google Colab
- Kaggle
thai_trocr_thaigov_v2
Vision Encoder Decoder Models
- Use microsoft/trocr-base-handwritten as encoder.
- Use airesearch/wangchanberta-base-att-spm-uncased as decoder
- Fine-tune on 250k synthetic text images dataset using ThaiGov V2 Corpus
- Use SynthTIGER to generate synthetic text image.
- It is useful to fine-tune any Thai OCR task.
Usage
from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
processor = TrOCRProcessor.from_pretrained("kkatiz/thai-trocr-thaigov-v2")
model = VisionEncoderDecoderModel.from_pretrained("kkatiz/thai-trocr-thaigov-v2")
image = Image.open("... your image path").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
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