number detection and recognition based on AdvancedEast and CRNN

Detection and Crop:

Recognition:

• 22046298859.jpg : 22046298859
• 97785067838.jpg : 97785067838
• 84999825604.jpg : 84999825604
• 99851544924.jpg : 99851544924
• 28510715459.jpg : 28510715459
• 12233418739.jpg : 12233418739
• 41679405336.jpg : 41679405336
• 37774346979.jpg : 37774346979

limitations: When the two models are test on their respective validation sets , they can reach an acc of about 0.9. However, the number of the training data for recognizer I generated is horizontal, and the number in the crop image after the detection result introduces the rotation and other factors, resulting in poor results when used in combination.

• prepare training data, data format refer to ICPR
• modify params in cfg.py
• run python preprocess.py to resize image and generator .npy training files
• run python label.py
• run python train.py, train the network

• modify your images' dir in predict.py, and run python predict.py, then we will get three outputs: bounding box on origin images, the cropped image, and coordinates(txt file).

more details please refer to AdvancedEast

• prepare training data, data format refer to MJSynth data
• modify params in cfg.py
• modify input_shape=(None, 50,7,512) in train.py line 55, the input_shape is refer to your bn_shape = bn4.get_shape() in network.py
• run python train.py

• modify your images' dir in predict.py, then run python predict.py