  Due to superstition, license plates with desirable combinations of characters
are highly sought after in China, fetching prices that can reach into the
millions in government-held auctions. Despite the high stakes involved, there
has been essentially no attempt to provide price estimates for license plates.
We present an end-to-end neural network model that simultaneously predict the
auction price, gives the distribution of prices and produces latent feature
vectors. While both types of neural network architectures we consider
outperform simpler machine learning methods, convolutional networks outperform
recurrent networks for comparable training time or model complexity. The
resulting model powers our online price estimator and search engine.
