minor grammatical fixes to dnn_custom_layers.md

doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md

@@ -3,29 +3,30 @@
 @prev_tutorial{tutorial_dnn_javascript}
 
 ## Introduction
-Deep learning is a fast growing area. The new approaches to build neural networks
+Deep learning is a fast-growing area. New approaches to building neural networks
-usually introduce new types of layers. They could be modifications of existing
+usually introduce new types of layers. These could be modifications of existing
-ones or implement outstanding researching ideas.
+ones or implementation of outstanding research ideas.
 
-OpenCV gives an opportunity to import and run networks from different deep learning
+OpenCV allows importing and running networks from different deep learning frameworks.
-frameworks. There are a number of the most popular layers. However you can face
+There is a number of the most popular layers. However, you can face a problem that
-a problem that your network cannot be imported using OpenCV because of unimplemented layers.
+your network cannot be imported using OpenCV because some layers of your network
+can be not implemented in the deep learning engine of OpenCV.
 
 The first solution is to create a feature request at https://github.com/opencv/opencv/issues
-mentioning details such a source of model and type of new layer. A new layer could
+mentioning details such as a source of a model and a type of new layer.
-be implemented if OpenCV community shares this need.
+The new layer could be implemented if the OpenCV community shares this need.
 
-The second way is to define a **custom layer** so OpenCV's deep learning engine
+The second way is to define a **custom layer** so that OpenCV's deep learning engine
 will know how to use it. This tutorial is dedicated to show you a process of deep
-learning models import customization.
+learning model's import customization.
 
 ## Define a custom layer in C++
 Deep learning layer is a building block of network's pipeline.
 It has connections to **input blobs** and produces results to **output blobs**.
 There are trained **weights** and **hyper-parameters**.
-Layers' names, types, weights and hyper-parameters are stored in files are generated by
+Layers' names, types, weights and hyper-parameters are stored in files are
-native frameworks during training. If OpenCV mets unknown layer type it throws an
+generated by native frameworks during training. If OpenCV encounters unknown
-exception trying to read a model:
+layer type it throws an exception while trying to read a model:
 
 ```
 Unspecified error: Can't create layer "layer_name" of type "MyType" in function getLayerInstance
@@ -61,7 +62,7 @@ This method should create an instance of you layer and return cv::Ptr with it.
 
 @snippet dnn/custom_layers.hpp MyLayer::getMemoryShapes
 
-Returns layer's output shapes depends on input shapes. You may request an extra
+Returns layer's output shapes depending on input shapes. You may request an extra
 memory using `internals`.
 
 - Run a layer
@@ -71,20 +72,20 @@ memory using `internals`.
 Implement a layer's logic here. Compute outputs for given inputs.
 
 @note OpenCV manages memory allocated for layers. In the most cases the same memory
-can be reused between layers. So your `forward` implementation should not rely that
+can be reused between layers. So your `forward` implementation should not rely on that
-the second invocation of `forward` will has the same data at `outputs` and `internals`.
+the second invocation of `forward` will have the same data at `outputs` and `internals`.
 
 - Optional `finalize` method
 
 @snippet dnn/custom_layers.hpp MyLayer::finalize
 
-The chain of methods are the following: OpenCV deep learning engine calls `create`
+The chain of methods is the following: OpenCV deep learning engine calls `create`
-method once then it calls `getMemoryShapes` for an every created layer then you
+method once, then it calls `getMemoryShapes` for every created layer, then you
-can make some preparations depends on known input dimensions at cv::dnn::Layer::finalize.
+can make some preparations depend on known input dimensions at cv::dnn::Layer::finalize.
-After network was initialized only `forward` method is called for an every network's input.
+After network was initialized only `forward` method is called for every network's input.
 
-@note Varying input blobs' sizes such height or width or batch size you make OpenCV
+@note Varying input blobs' sizes such height, width or batch size make OpenCV
-reallocate all the internal memory. That leads efficiency gaps. Try to initialize
+reallocate all the internal memory. That leads to efficiency gaps. Try to initialize
 and deploy models using a fixed batch size and image's dimensions.
 
 ## Example: custom layer from Caffe
@@ -201,7 +202,7 @@ deep learning model. That was trained with one and only difference comparing to
 a current version of [Caffe framework](http://caffe.berkeleyvision.org/). `Crop`
 layers that receive two input blobs and crop the first one to match spatial dimensions
 of the second one used to crop from the center. Nowadays Caffe's layer does it
-from the top-left corner. So using the latest version of Caffe or OpenCV you'll
+from the top-left corner. So using the latest version of Caffe or OpenCV you will
 get shifted results with filled borders.
 
 Next we're going to replace OpenCV's `Crop` layer that makes top-left cropping by
@@ -217,7 +218,7 @@ a centric one.
 
 @snippet dnn/edge_detection.py Register
 
-That's it! We've replaced an implemented OpenCV's layer to a custom one.
+That's it! We have replaced an implemented OpenCV's layer to a custom one.
 You may find a full script in the [source code](https://github.com/opencv/opencv/tree/3.4/samples/dnn/edge_detection.py).
 
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