Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text
  Recognition   Recognizing text in the wild is a really challenging task because of complex
backgrounds, various illuminations and diverse distortions, even with deep
neural networks (convolutional neural networks and recurrent neural networks).
In the end-to-end training procedure for scene text recognition, the outputs of
deep neural networks at different iterations are always demonstrated with
diversity and complementarity for the target object (text). Here, a simple but
effective deep learning method, an adaptive ensemble of deep neural networks
(AdaDNNs), is proposed to simply select and adaptively combine classifier
components at different iterations from the whole learning system. Furthermore,
the ensemble is formulated as a Bayesian framework for classifier weighting and
combination. A variety of experiments on several typical acknowledged
benchmarks, i.e., ICDAR Robust Reading Competition (Challenge 1, 2 and 4)
datasets, verify the surprised improvement from the baseline DNNs, and the
effectiveness of AdaDNNs compared with the recent state-of-the-art methods.

28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat