Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards End-to-end Text Spotting with Convolutional Recurrent Neural
  Networks   In this work, we jointly address the problem of text detection and
recognition in natural scene images based on convolutional recurrent neural
networks. We propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
like image cropping and feature re-calculation, word separation, or character
grouping. In contrast to existing approaches that consider text detection and
recognition as two distinct tasks and tackle them one by one, the proposed
framework settles these two tasks concurrently. The whole framework can be
trained end-to-end, requiring only images, the ground-truth bounding boxes and
text labels. Through end-to-end training, the learned features can be more
informative, which improves the overall performance. The convolutional features
are calculated only once and shared by both detection and recognition, which
saves processing time. Our proposed method has achieved competitive performance
on several benchmark datasets.

9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga