Merge pull request #4118 from kuaashish:master

PiperOrigin-RevId: 513364683

docs/solutions/face_mesh.md

@@ -143,7 +143,7 @@ about the model in this [paper](https://arxiv.org/abs/2006.10962).
 The [Face Landmark Model](#face-landmark-model) performs a single-camera face landmark
 detection in the screen coordinate space: the X- and Y- coordinates are
 normalized screen coordinates, while the Z coordinate is relative and is scaled
-as the X coodinate under the
+as the X coordinate under the
 [weak perspective projection camera model](https://en.wikipedia.org/wiki/3D_projection#Weak_perspective_projection).
 This format is well-suited for some applications, however it does not directly
 enable the full spectrum of augmented reality (AR) features like aligning a

docs/solutions/iris.md

@@ -48,7 +48,7 @@ camera, in real-time, without the need for specialized hardware. Through use of
 iris landmarks, the solution is also able to determine the metric distance
 between the subject and the camera with relative error less than 10%. Note that
 iris tracking does not infer the location at which people are looking, nor does
-it provide any form of identity recognition. With the cross-platfrom capability
+it provide any form of identity recognition. With the cross-platform capability
 of the MediaPipe framework, MediaPipe Iris can run on most modern
 [mobile phones](#mobile), [desktops/laptops](#desktop) and even on the
 [web](#web).
@@ -109,7 +109,7 @@ You can also find more details in this
 ### Iris Landmark Model
 
 The iris model takes an image patch of the eye region and estimates both the eye
-landmarks (along the eyelid) and iris landmarks (along ths iris contour). You
+landmarks (along the eyelid) and iris landmarks (along this iris contour). You
 can find more details in this [paper](https://arxiv.org/abs/2006.11341).
 
 ![iris_tracking_eye_and_iris_landmarks.png](https://mediapipe.dev/images/mobile/iris_tracking_eye_and_iris_landmarks.png) |

docs/solutions/media_sequence.md

@@ -95,7 +95,7 @@ process new data sets, in the documentation of
 
     MediaSequence uses SequenceExamples as the format of both inputs and
     outputs. Annotations are encoded as inputs in a SequenceExample of metadata
-    that defines the labels and the path to the cooresponding video file. This
+    that defines the labels and the path to the corresponding video file. This
     metadata is passed as input to the C++ `media_sequence_demo` binary, and the
     output is a SequenceExample filled with images and annotations ready for
     model training.

docs/solutions/objectron.md

@@ -180,7 +180,7 @@ and a
 The detection subgraph performs ML inference only once every few frames to
 reduce computation load, and decodes the output tensor to a FrameAnnotation that
 contains nine keypoints: the 3D bounding box's center and its eight vertices.
-The tracking subgraph runs every frame, using the box traker in
+The tracking subgraph runs every frame, using the box tracker in
 [MediaPipe Box Tracking](./box_tracking.md) to track the 2D box tightly
 enclosing the projection of the 3D bounding box, and lifts the tracked 2D
 keypoints to 3D with
@@ -623,7 +623,7 @@ z_ndc = 1 / Z
 
 ### Pixel Space
 
-In this API we set upper-left coner of an image as the origin of pixel
+In this API we set upper-left corner of an image as the origin of pixel
 coordinate. One can convert from NDC to pixel space as follows:
 
 ```