Reading text photographs is a challenging

problem that has received a significant amount of attention.

Two key components of most systems are (i) text detection from

images and (ii) character recognition, and many recent methods

have been proposed to design better feature representations

and models for both. In this paper, we apply methods recently

developed in machine learning–specifically, large-scale algorithms

for learning the features automatically unlabeled

data–and show that they allow us to construct highly effective

classifiers for both detection and recognition to be used in a

high accuracy end-to-end system.-Reading text　photographs is a challenging

problem that has received a significant amount of attention.

Two key components of most systems are (i) text detection from

images and (ii) character recognition, and many recent methods

have been proposed to design better feature representations

and models for both. In this paper, we apply methods recently

developed in machine learning–specifically, large-scale algorithms

for learning the features automatically　unlabeled

data–and show that they allow us to construct highly effective

classifiers for both detection and recognition to be used in a

high accuracy end-to-end system.