The ViTLP model was proposed in Visually Guided Generative Text-Layout Pre-training for Document Intelligence, which is a generative foundation model for document intelligence. We provide the pre-trained checkpoint ViTLP-medium (380M). The pre-trained ViTLP model can natively perform OCR text localization and recognition.

The code of ViTLP inference and demo is assisible at https://github.com/Veason-silverbullet/ViTLP.

When I commenced this project, it was on the eve of LLMs (precisely speaking, ChatGPT). ViTLP-base presented in our paper, is actually a rather small pre-trained model. We know it is expected to scale up ViTLP in this LLM era. However, the pre-training scale is commonly constrained by computation resources and the pre-training dataset scale, in which context ViTLP-medium (380M) is the largest pre-training scale so far we can support.

Besides, this scale of ViTLP also brings inference sweetness including speed and memory usage. Typically, OCR on a page of a document image can be processed within 5~10 seconds in an Nvidia 4090, which is comparable to (and faster than) most OCR engines (and LLMs).

ViTLP is pronounced /ˈvai·tlp/ (vital). The first version of our paper was submitted to OpenReview in June 2023.