Hi, this is my GSoC project to add digit and text recognition samples.

Status Update:

The detailed tutorial of OCR models usage method and how to train your own OCR model have been added to the doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown.

1. digit recognition

Take the live image from the camera, use connected component analysis to detect potential regions with each digit, and use the LeNet to classify.
With CPU only (i5-8300), it can achieve 12 FPS.

2. scene text recognition

My laptop environment is CPU: i5-8300, GPU: 1050, Ubuntu 18
Take the live image from the camera, use EAST as text detector. After getting the detector output, crop these bounding box as the input of the text recognizer based on VGG Net. Finally, print the result near the box. Using GPU can achieve around 9FPS.

After loading the model into OpenCV, test the performance of the text recognition model on different data sets. And the result is filled in following table:

These pre-trained models can be found here https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing. The FPS in the table is the performance of the text recognition model on my computer, and does not include the text detection model.

Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under OpenCV (BSD) License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
• The PR is proposed to proper branch
• There is reference to original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake