title: Pre Trained LeNet Model for License plate Recognition
body: <p>I have implemented a form of the LeNet model via <strong>tensorflow and python</strong> for a <strong>Car number plate recognition system</strong>. My model was trained solely on my train data and tested on the test data. <strong>My dataset contains segmented images wherein every image has only one character in them.</strong> This is what my <a href="https://stackoverflow.com/questions/50713023/license-plate-character-segmentation-python-opencv">data</a> looks like. My created model does not perform very well, so I'm now looking for models which I can use via Transfer Learning. Since most models, are already trained on a humongous dataset, I looked over a few like AlexNet, ResNet, GoogLeNet and Inception v2. Most of these models have not been trained on the type of data that I want which would be, <strong>Letters and digits.</strong> </p>
  
  <p><strong>Question:</strong> Should I still go forward with one of these models and train them on my dataset or are there any better models which would help ? For such models would keras be a better option since it is more high level than Tensorflow? </p>
  
  <p><strong>Question:</strong> I'd prefer to work with the LeNet model itself since training the other models would definitely take a long time due to the insufficient specs of my laptop. So is there any implementation of the model which uses machine printed character images to train the model which I could use to then train the final layers of the model on my data? </p>