  Typical text recognition methods rely on an encoder-decoder structure, in
which the encoder extracts features from an image, and the decoder produces
recognized text from these features. In this study, we propose a simpler and
more effective method for text recognition, known as the Decoder-only
Transformer for Optical Character Recognition (DTrOCR). This method uses a
decoder-only Transformer to take advantage of a generative language model that
is pre-trained on a large corpus. We examined whether a generative language
model that has been successful in natural language processing can also be
effective for text recognition in computer vision. Our experiments demonstrated
that DTrOCR outperforms current state-of-the-art methods by a large margin in
the recognition of printed, handwritten, and scene text in both English and
Chinese.
