

Use case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False
In case one wants to do OCR themselves, one can initialize the image processor with apply_ocr set to
False. In that case, one should provide the words and corresponding (normalized) bounding boxes themselves to
the processor.
thon
from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
).convert("RGB")
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
encoding = processor(image, words, boxes=boxes, return_tensors="pt")
print(encoding.keys())
dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])