Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NENET: An Edge Learnable Network for Link Prediction in Scene Text   Text detection in scenes based on deep neural networks have shown promising
results. Instead of using word bounding box regression, recent state-of-the-art
methods have started focusing on character bounding box and pixel-level
prediction. This necessitates the need to link adjacent characters, which we
propose in this paper using a novel Graph Neural Network (GNN) architecture
that allows us to learn both node and edge features as opposed to only the node
features under the typical GNN. The main advantage of using GNN for link
prediction lies in its ability to connect characters which are spatially
separated and have an arbitrary orientation. We show our concept on the well
known SynthText dataset, achieving top results as compared to state-of-the-art
methods.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat