diff --git a/docs/source/en/preprocessing.md b/docs/source/en/preprocessing.md
index 4aa9030fe..04e9688c9 100644
--- a/docs/source/en/preprocessing.md
+++ b/docs/source/en/preprocessing.md
@@ -22,7 +22,7 @@ Before you can train a model on a dataset, it needs to be preprocessed into the
 
 * Text, use a [Tokenizer](./main_classes/tokenizer) to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
 * Speech and audio, use a [Feature extractor](./main_classes/feature_extractor) to extract sequential features from audio waveforms and convert them into tensors.
-* Image inputs use a [ImageProcessor](./main_classes/image) to convert images into tensors.
+* Image inputs use a [ImageProcessor](./main_classes/image_processor) to convert images into tensors.
 * Multimodal inputs, use a [Processor](./main_classes/processors) to combine a tokenizer and a feature extractor or image processor.
 
 <Tip>
@@ -397,7 +397,7 @@ width are expected, for others only the `shortest_edge` is defined.
 >>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])
 ```
 
-2. The model accepts [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values)
+2. The model accepts [`pixel_values`](model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel.forward.pixel_values)
 as its input. `ImageProcessor` can take care of normalizing the images, and generating appropriate tensors.
 Create a function that combines image augmentation and image preprocessing for a batch of images and generates `pixel_values`: