To solve this test problem, I have used single-shot multi-object detection model (SSD -300) for reading the encoded text and identifying its location in the test image. SSD model is based on VGG-16 and the approach defined in this paper by Wei Liu.

Dataset used to train the model is Street View House Number dataset. (SVHN) Model is trained to detect digits from 0 to 8 but not 9. (Reason: Rotation augmentation is used and 9 is detected as 6).

Training set used = SVHN Training set +SVHN Extra Training Set

I have used Python 3.6.5 and Tensorflow 1.12.

• Step 1 Run train.py file

The Program will take approx 45 min. to download SVHN dataset and start training. The hyper-parameter values used in the program are.

• Batch size = 32

• Learning rate

• Momentum parameter for Momentum optimizer = 0.9

• L2 regularization factor = 0.0005

• Probability Threshold = 0.5

• Category to be classified by neural network = 9 (digits) + 1 (background) + 4 (location coordinate)=14

• Step 2 Run train_restore.py

In case train.py break, run train_restore.py. Changes to be done in every restore are.

• Update latest metadata file name to restore program to start training.

• And update Step parameter value to continue training.

• Step 3 Run detect.py

• To check results of training on Test set. Some more sample images from Test set can be found in folder More_Result_Images in repository.

• Step 4 Run detect_robot1.py

To get same result shown in Readme.md file above. Update latest metadata file name to read text from the image robot1.png.

Check Results:

• My latest trained model.chkt-final files can be downloaded from here.

• detect_robot1.py can be run directly to get same results.