Merge pull request #17570 from HannibalAPE:text_det_recog_demo

[GSoC] High Level API and Samples for Scene Text Detection and Recognition

* APIs and samples for scene text detection and recognition

* update APIs and tutorial for Text Detection and Recognition

* API updates:
(1) put decodeType into struct Voc
(2) optimize the post-processing of DB

* sample update:
(1) add transformation into scene_text_spotting.cpp
(2) modify text_detection.cpp with API update

* update tutorial

* simplify text recognition API
update tutorial

* update impl usage in recognize() and detect()

* dnn: refactoring public API of TextRecognitionModel/TextDetectionModel

* update provided models
update opencv.bib

* dnn: adjust text rectangle angle

* remove points ordering operation in model.cpp

* update gts of DB test in test_model.cpp

* dnn: ensure to keep text rectangle angle

- avoid 90/180 degree turns

* dnn(text): use quadrangle result in TextDetectionModel API

* dnn: update Text Detection API
(1) keep points' order consistent with (bl, tl, tr, br) in unclip
(2) update contourScore with boundingRect

doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown

@@ -1,6 +1,7 @@
 # How to run custom OCR model {#tutorial_dnn_OCR}
 
 @prev_tutorial{tutorial_dnn_custom_layers}
+@next_tutorial{tutorial_dnn_text_spotting}
 
 ## Introduction
 
@@ -43,4 +44,4 @@ The input of text recognition model is the output of the text detection model, w
 
 DenseNet_CTC has the smallest parameters and best FPS, and it is suitable for edge devices, which are very sensitive to the cost of calculation. If you have limited computing resources and want to achieve better accuracy, VGG_CTC is a good choice.
 
-CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.
+CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.