TITLE:
Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words
AUTHORS:
Esam Qaralleh, Gheith Abandah, Fuad Jamour
KEYWORDS:
Optical Character Recognition; Handwritten Arabic Words; Recurrent Neural Networks; Design of Experiments
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.6 No.10,
October
4,
2013
ABSTRACT: Artificial neural networks have the
abilities to learn by example and are capable of solving problems that are hard
to solve using ordinary rule-based programming. They have many design parameters
that affect their performance such as the number and sizes of the hidden
layers. Large sizes are slow and small sizes are generally not accurate. Tuning
the neural network size is a hard task because the design space is often large
and training is often a long process. We use design of experiments techniques
to tune the recurrent neural network used in an Arabic handwriting recognition
system. We show that best results are achieved with three hidden layers and two
subsampling layers. To tune the sizes of these five layers, we use fractional
factorial experiment design to limit the number of experiments to a feasible
number. Moreover, we replicate the experiment configuration multiple times to overcome the randomness in
the training process. The accuracy and time measurements are analyzed and
modeled. The two models are then used to locate network sizes that are on the
Pareto optimal frontier. The approach described in this paper reduces the label
error from 26.2% to 19.8%.