final_result_prediction.py

# pre-processing input images and pedict with model
def predict_from_model(image,model,labels):
    image = cv2.resize(image,(80,80))
    image = np.stack((image,)*3, axis=-1)
    prediction = labels.inverse_transform([np.argmax(model.predict(image[np.newaxis,:]))])
    return prediction
    
fig = plt.figure(figsize=(15,3))
cols = len(crop_characters)
grid = gridspec.GridSpec(ncols=cols,nrows=1,figure=fig)

final_string = ''
for i,character in enumerate(crop_characters):
    fig.add_subplot(grid[i])
    title = np.array2string(predict_from_model(character,model,labels))
    plt.title('{}'.format(title.strip("'[]"),fontsize=20))
    final_string+=title.strip("'[]")
    plt.axis(False)
    plt.imshow(character,cmap='gray')

print("Achieved result: ", final_string)
#plt.savefig('final_result.png', dpi=300)