RNN-LSTM based beta-elliptic model for online handwriting script identification

Joint Authors

Zouari, Ramzi
Bu Bakr, Husayn
Khayr Allah, Munji

Source

The International Arab Journal of Information Technology

Publisher

Zarqa University

Publication Date

2018-05-31

Country of Publication

Jordan

No. of Pages

8

Main Subjects

Information Technology and Computer Science

English Abstract

Recurrent Neural Network (RNN) has achieved the state-of-the-art performance in a wide range of applications dealing with sequential input data.

In this context, the proposed system aims to classify the online handwriting scripts based on their labelled pseudo-words.

To avoid the vanishing gradient problem, we have used a variant of recurrent network with Long Short-Term Memory.

The representation of the sequential aspect of the data is done through the beta-elliptic model.

It allows extracting the dynamics and kinematics profiles of different strokes constituting a script over the time.

This system was assessed with a large vocabulary containing scripts from ADAB, UNIPEN and PENDIGIT databases.

The experiments results show the effectiveness of the proposed system which reached a high recognition rate with only one recurrent layer and using the dropout technique.

Data Type

Conference Papers

Record ID

BIM-896576

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