  We propose a novel method that uses convolutional neural networks (CNNs) for
feature extraction. Not just limited to conventional spatial domain
representation, we use multilevel 2D discrete Haar wavelet transform, where
image representations are scaled to a variety of different sizes. These are
then used to train different CNNs to select features. To be precise, we use 10
different CNNs that select a set of 10240 features, i.e. 1024/CNN. With this,
11 different handwritten scripts are identified, where 1K words per script are
used. In our test, we have achieved the maximum script identification rate of
94.73% using multi-layer perceptron (MLP). Our results outperform the
state-of-the-art techniques.
