How can I use the Keras OCR example?

Question

I found examples/image_ocr.py which seems to for OCR. Hence it should be possible to give the model an image and receive text. However, I have no idea how to do so. How do I

Answer 1

Here, you created a model that needs 4 inputs:

model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)

Your predict attempt, on the other hand, is loading just an image.
Hence the message: The model expects 4 arrays, but only received one array

From your code, the necessary inputs are:

input_data = Input(name='the_input', shape=input_shape, dtype='float32')
labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len],dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')

The original code and your training work because they're using the TextImageGenerator. This generator cares to give you the four necessary inputs for the model.

So, what you have to do is to predict using the generator. As you have the fit_generator() method for training with the generator, you also have the predict_generator() method for predicting with the generator.

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Now, for a complete answer and solution, I'd have to study your generator and see how it works (which would take me some time). But now you know what is to be done, you can probably figure it out.

You can either use the generator as it is, and predict probably a huge lot of data, or you can try to replicate a generator that will yield just one or a few images with the necessary labels, length and label length.

Or maybe, if possible, just create the 3 remaining arrays manually, but making sure they have the same shapes (except for the first, which is the batch size) as the generator outputs.

The one thing you must assert, though, is: have 4 arrays with the same shapes as the generator outputs, except for the first dimension.