potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages Releases Wiki Activity

Merge pull request #25042 from mshabunin:doc-upgrade

Browse Source 
Documentation transition to fresh Doxygen #25042

* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
This commit is contained in:
Maksim Shabunin
2024-03-05 16:19:45 +03:00
committed by GitHub
parent 0f5792a7a1
commit bf06e3d09f
49 changed files with 1764 additions and 1658 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown
+4 -3
View File
@@ -22,7 +22,7 @@ In this tutorial, we first introduce how to obtain the custom OCR model, then ho
After completing the model training, please use [transform_to_onnx.py](https://github.com/zihaomu/deep-text-recognition-benchmark/blob/master/transform_to_onnx.py) to convert the model into onnx format.
#### Execute in webcam
### Execute in webcam
The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/4.x/samples/dnn/text_detection.py).
Example:
@@ -44,9 +44,10 @@ Their performance at different text recognition datasets is shown in the table b
| CRNN_VGG-BiLSTM-CTC | 82.63 | 82.07 | 92.96 | 88.867 | 66.28 | 71.01 | 62.37 | 78.03 | 8.45 |
| ResNet-CTC | 84.00 | 84.08 | 92.39 | 88.96 | 67.74 | 74.73 | 67.60 | 79.93 | 44.28 |
The performance of the text recognition model were tesred on OpenCV DNN, and does not include the text detection model.
The performance of the text recognition model were tested on OpenCV DNN, and does not include the text detection model.
### Model selection suggestion
#### Model selection suggestion:
The input of text recognition model is the output of the text detection model, which causes the performance of text detection to greatly affect the performance of text recognition.
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.