Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks

Understand

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges.

• One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge.
• To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition.
• In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy.