feat: add rotation in docs

docs/source/using_models.rst

@@ -17,6 +17,7 @@ Text Detection
 
 The task consists of localizing textual elements in a given image.
 While those text elements can represent many things, in docTR, we will consider uninterrupted character sequences (words). Additionally, the localization can take several forms: from straight bounding boxes (delimited by the 2D coordinates of the top-left and bottom-right corner), to polygons, or binary segmentation (flagging which pixels belong to this element, and which don't).
+Our latest detection models works with rotated and skewed documents!
 
 Available architectures
 ^^^^^^^^^^^^^^^^^^^^^^^
@@ -27,6 +28,10 @@ The following architectures are currently supported:
 * `db_resnet50 <models.html#doctr.models.detection.db_resnet50>`_
 * `db_mobilenet_v3_large <models.html#doctr.models.detection.db_mobilenet_v3_large>`_
 
+We also provide 2 models working with any kind of rotated document:
+* `linknet_resnet18_rotation <models.html#doctr.models.detection.linknet_resnet18_rotation>`_
+* `db_resnet50_rotation <models.html#doctr.models.detection.db_resnet50_rotation>`_
+
 For a comprehensive comparison, we have compiled a detailed benchmark on publicly available datasets:
 
 
@@ -60,6 +65,19 @@ Detection predictors
     >>> dummy_img = (255 * np.random.rand(800, 600, 3)).astype(np.uint8)
     >>> out = model([dummy_img])
 
+You can pass specific boolean arguments into the predictor:
+
+* `assume_straight_pages`: if you work with straight documents only, it will fit straight bounding boxes to the text areas.
+* `preserve_aspect_ratio`: if you want to preserve the aspect ratio of your documents while resizing before sending them to the model.
+* `symmetric_pad`: if you choose to preserve the aspect ratio, it will pas the image symmetrically and not from the bottom-right.
+
+For instance, this snippet will instantiate a detection predictor able to detect text on rotated documents while preserving the aspect ratio:
+
+    >>> from doctr.models import detection_predictor  
+    >>> predictor = detection_predictor('db_resnet50_rotation', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True)
+
+NB: for the moment, `db_resnet50_rotation` is pretrained in Pytorch only and `linknet_resnet18_rotation` in Tensorflow only.
+
 
 Text Recognition
 ----------------
@@ -228,6 +246,22 @@ Those architectures involve one stage of text detection, and one stage of text r
     >>> out = model([input_page])
 
 
+You can pass specific boolean arguments into the predictor:
+
+* `assume_straight_pages`
+* `preserve_aspect_ratio`
+* `symmetric_pad`
+
+Those 3 are going straight to the detection predictor, as mentioned above (in the detection part).
+
+* `export_as_straight_boxes`: If you work with rotated and skewed documents but you still want to export straight boundong boxes and not polygons, set to True.
+
+For instance, this snippet instantiate a end-to-end ocr_predictor working with rotated documents, which preserves the aspect ratio of the doucments, and returns polygons:
+
+    >>> from doctr.model import ocr_predictor
+    >>> model = ocr_predictor('linknet_resnet18_rotation', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True)
+
+
 What should I do with the output?
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^