TrOCR
TrOCR
**TrOCR** is an end-to-end [Transformer](https://paperswithcode.com/methods/category/transformers)-based OCR model for text recognition with pre-trained CV and NLP models. It leverages the [Transformer](https://paperswithcode.com/method/transformer) architecture for both image understanding and wordpiece-level text generation. It first resizes the input text image into $384 × 384$ and then the image is split into a sequence of 16 patches which are used as the input to image Transformers.  Standard Transformer architecture with the [self-attention mechanism](https://paperswithcode.com/method/scaled) is leveraged on both encoder and decoder parts, where wordpiece units are generated as the recognized text from the input image.
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://paperswithcode.com/paper/trocr-transformer-based-optical-character
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models