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IEEE/ICACT20240477 Slide.06        [Big Slide]       [YouTube]	Oral Presentation
	Using truncation for lengthier inputs, padding for uniform input length, and label indexing via dictionaries (idx2label and label2idx) for the numerical representation of class labels, we employed a variety of Hugging Face Transformers Libraries. While the model architecture is the same in all versions, LayoutLMv2 leverages visual information early in the training process by integrating Tesseract OCR and visual embeddings during pre-training.

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