Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HWRCNet: Handwritten Word Recognition in JPEG Compressed Domain using
  CNN-BiLSTM Network   The handwritten word recognition from images using deep learning is an active
research area with promising performance. It practical scenario, it might be
required to process the handwritten images in the compressed domain due to due
to security reasons. However, the utilization of deep learning is still very
limited for the processing of compressed images. Motivated by the need of
processing document images in the compressed domain using recent developments
in deep learning, we propose a HWRCNet model for handwritten word recognition
in JPEG compressed domain. The proposed model combines the Convolutional Neural
Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) based
Recurrent Neural Network (RNN). Basically, we train the model using compressed
domain images and observe a very appealing performance with 89.05% word
recognition accuracy and 13.37% character error rate.

8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas