  Text recognition is a long-standing research problem for document
digitalization. Existing approaches 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 show that the TrOCR model
outperforms the current state-of-the-art models on the printed, handwritten and
scene text recognition tasks. The TrOCR models and code are publicly available
at \url{https://aka.ms/trocr}.
