Typos

demos/python_demos/image_retrieval_demo/README.md

@@ -9,7 +9,7 @@ This demo demonstrates how to run Image Retrieval models using OpenVINO™.
 The demo application expects an image retrieval model in the Intermediate Representation (IR) format.
 
 As input, the demo application takes:
-* a path to a list of images represeneted by textfile with following format 'path_to_image' 'ID' `--images`
+* a path to a list of images represented by textfile with following format 'path_to_image' 'ID' `--images`
 * a path to a video file or a device node of a web-camera specified with a command line argument `--video`
 
 The demo workflow is the following:

demos/security_barrier_camera_demo/README.md

@@ -98,7 +98,7 @@ To do inference for two video inputs using two asynchronous infer request on FPG
 ./security_barrier_camera_demo -i <path_to_video>/inputVideo_0.mp4 <path_to_video>/inputVideo_1.mp4 -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d HETERO:FPGA,CPU -d_va HETERO:FPGA,CPU -d_lpr HETERO:FPGA,CPU -nireq 2
 ```
 
-> **NOTE**: For the `-tag` option (HDDL plugin only), you must specify the number of VPUs for each network in the `hddl_service.config` file located in the `<INSTALL_DIR>/deployment_tools/inference_engine/external/hddl/config/` direcrtory using the following tags:
+> **NOTE**: For the `-tag` option (HDDL plugin only), you must specify the number of VPUs for each network in the `hddl_service.config` file located in the `<INSTALL_DIR>/deployment_tools/inference_engine/external/hddl/config/` directory using the following tags:
 > * `tagDetect` for the Vehicle and License Plate Detection network
 > * `tagAttr` for the Vehicle Attributes Recognition network
 > * `tagLPR` for the License Plate Recognition network
@@ -114,7 +114,7 @@ To do inference for two video inputs using two asynchronous infer request on FPG
 
 ### Optimization Hints for Heterogeneous Scenarios with FPGA
 
-If you build the Inference Engine with the OMP, you can use the following parameters for Heterogeneous scenarois:
+If you build the Inference Engine with the OMP, you can use the following parameters for Heterogeneous scenarios:
 
 * `OMP_NUM_THREADS`: Specifies number of threads to use. For heterogeneous scenarios with FPGA, when several inference requests are used asynchronously, limiting the number of CPU threads with `OMP_NUM_THREADS` allows to avoid competing for resources between threads. For the Security Barrier Camera Demo, recommended value is `OMP_NUM_THREADS=1`.
 * `KMP_BLOCKTIME`: Sets the time, in milliseconds, that a thread should wait, after completing the execution of a parallel region, before sleeping. The default value is 200ms, which is not optimal for the demo. Recommended value is `KMP_BLOCKTIME=1`.

demos/text_detection_demo/README.md

@@ -32,7 +32,7 @@ Options:
     -m_tr "<path>"               Required. Path to the Text Recognition model (.xml) file.
     -dt "<input_data_type>"      Required. Input data type: "image" (for a single image), "list" (for a text file where images paths are listed), "video" (for a saved video), "webcam" (for a webcamera device). By default, it is "image".
     -m_tr_ss "<value>"           Optional. Symbol set for the Text Recognition model.
-    -cc                          Optional. If it is set, then in case of absence of the Text Detector, the Text Reconition model takes a central image crop as an input, but not full frame.
+    -cc                          Optional. If it is set, then in case of absence of the Text Detector, the Text Recognition model takes a central image crop as an input, but not full frame.
     -w_td "<value>"              Optional. Input image width for Text Detection model.
     -h_td "<value>"              Optional. Input image height for Text Detection model.
     -thr "<value>"               Optional. Specify a recognition confidence threshold. Text detection candidates with text recognition confidence below specified threshold are rejected.

demos/text_detection_demo/text_detection_demo.hpp

@@ -16,7 +16,7 @@ static const char text_detection_model_message[] = "Required. Path to the Text D
 static const char text_recognition_model_message[] = "Required. Path to the Text Recognition model (.xml) file.";
 static const char text_recognition_model_symbols_set_message[] = "Optional. Symbol set for the Text Recognition model.";
 static const char text_central_image_crop_message[] = "Optional. If it is set, then in case of absence of the Text Detector, "
-                                                      "the Text Reconition model takes a central image crop as an input, but not full frame.";
+                                                      "the Text Recognition model takes a central image crop as an input, but not full frame.";
 static const char image_width_for_text_detection_model_message[] = "Optional. Input image width for Text Detection model.";
 static const char image_height_for_text_detection_model_message[] = "Optional. Input image height for Text Detection model.";
 static const char text_recognition_threshold_message[] = "Optional. Specify a recognition confidence threshold. Text detection candidates with "

models/intel/person-attributes-recognition-crossroad-0230/description/person-attributes-recognition-crossroad-0230.md

@@ -1,7 +1,7 @@
 # person-attributes-recognition-crossroad-0230
 
 ## Use Case and High-Level Description
-This model presents a person attributes classification algorithm analysis scenario. It produces probability of person attributions existing on the sample and a position of two point on sample, whiches can be used for color prob (like, color picker in graphical editors)
+This model presents a person attributes classification algorithm analysis scenario. It produces probability of person attributions existing on the sample and a position of two point on sample, which can be used for color prob (like, color picker in graphical editors)
 
 ## Examples
 

models/intel/person-attributes-recognition-crossroad-0230/model.yml

@@ -15,7 +15,7 @@
 description: >-
   This model presents a person attributes classification algorithm analysis scenario.
   It produces probability of person attributions existing on the sample and a
-  position of two point on sample, whiches can be used for color prob (like, color
+  position of two point on sample, which can be used for color prob (like, color
   picker in graphical editors)
 task_type: object_attributes
 files: