Classifying Letters using CNN - Help

Question

so some context, I'm trying to develop an OCR (for fun) and for that reason I decided to first find text within a page, parse it in to letters within the text and from there try and classify the letters that were extracted one by one.

For the classification I'm trying to build a CNN based upon the following database: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/

and the following CNN architecture:

 # CNN architecture
input = Input(shape=(32, 32, 1))
conv1 = Conv2D(32, (3, 3))(input)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
drop1 = Dropout(0.5)(pool1)
conv2 = Conv2D(64, (3, 3))(drop1)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
drop2 = Dropout(0.5)(pool2)
flatten = Flatten()(drop2)
fully_connected = Dense(512, activation='relu')(flatten)
output = Dense(62)(fully_connected)
model = Model(inputs=input, outputs=output)
model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam', metrics=['accuracy'])
print(model.summary())
model.fit(X, y, epochs=10 , validation_split=0.3)

However, all I seen to get no matter what I try (epochs, batch size, validation split) etc is 0 accuracy.... So assuming my database and labels are fine... what could be going wrong?

Answer 1

So there are some things you could consider.

First of all, normally datasets like this (or for example mnist) will be one hot encoded. So you have a vector with the length of all classes, each position stands for one class and is 1 if it is the class and 0 otherwise. As you are predicting values between zero and one, the activation of your output should do the same. So the first thing i would change is the activation function of the last layer to sigmoid. It is linear in your case.

Other things to consider are data preprocessing like normalizing pixel values between 0 and 1. Also no data preprocessing at all can lead to unstable training it is unlikely to cause no learning at all.