perform optical character recognition (OCR).
The abstract from the paper is the following:
Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition
are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language
model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end
text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the
Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but
effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments
