  Deep learning models have been used extensively to solve real-world problems
in recent years. The performance of such models relies heavily on large amounts
of labeled data for training. While the advances of data collection technology
have enabled the acquisition of a massive volume of data, labeling the data
remains an expensive and time-consuming task. Active learning techniques are
being progressively adopted to accelerate the development of machine learning
solutions by allowing the model to query the data they learn from. In this
paper, we introduce a real-world problem, the recognition of parking signs, and
present a framework that combines active learning techniques with a transfer
learning approach and crowd-sourcing tools to create and train a machine
learning solution to the problem. We discuss how such a framework contributes
to building an accurate model in a cost-effective and fast way to solve the
parking sign recognition problem in spite of the unevenness of the data
associated with the fact that street-level images (such as parking signs) vary
in shape, color, orientation and scale, and often appear on top of different
types of background.
