文件名称:2-learning
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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.
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.
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2 learning.pdf