Update README.md

Author: NielsRogge

LayoutLMv3/README.md

@@ -17,7 +17,7 @@ Note that LayoutLMv3 is identical to LayoutLMv2 in terms of training/inference,
 * images need to be resized and normalized, such that they are `pixel_values` of shape `(batch_size, num_channels, heigth, width)`. The channels need to be in RGB format. This was not the case for LayoutLMv2, which expected the channels in BGR format (due to its Detectron2 visual backbone), and normalized the images internally.
 * tokenization of text is based on RoBERTa, hence byte-level Byte-Pair-Encoding. This in contrast to LayoutLMv2, which used BERT-like WordPiece tokenization.
 
-Because of this, I've created a new `LayoutLMv3Processor`, which combines a `LayoutLMv3FeatureExtractor` (for the image modality) and a `LayoutLMv3TokenizerFast` (for the text modality) into one. Usage is identical to its predecessor [`LayoutLMv2Processor`](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor).
+Because of this, I've created a new `LayoutLMv3Processor`, which combines a `LayoutLMv3ImageProcessor` (for the image modality) and a `LayoutLMv3TokenizerFast` (for the text modality) into one. Usage is identical to its predecessor [`LayoutLMv2Processor`](https://huggingface.co/docs/transformers/model_doc/layoutlmv2#usage-layoutlmv2processor).
 
 The full documentation can be found [here](https://huggingface.co/transformers/model_doc/layoutlmv3.html).