Impact of struck-out text on writer identification

Document Type

Conference Article

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Abstract

The presence of struck-out text in handwritten manuscripts may affect the accuracy of automated writer identification. This paper presents a study on such effects of struck-out text. Here we consider offline English and Bengali handwritten document images. At first, the struck-out texts are detected using a hybrid classifier of a CNN (Convolutional Neural Network) and an SVM (Support Vector Machine). Then the writer identification process is activated on normal and struck-out text separately, to ascertain the impact of struck-out texts. For writer identification, we use two methods: (a) a hand-crafted feature-based SVM classifier, and (b) CNN-extracted auto-derived features with a recurrent neural model. For the experimental analysis, we have generated a database from 100 English and 100 Bengali writers. The performance of our system is very encouraging.

First Page

1465

Last Page

1471

DOI

10.1109/IJCNN.2017.7966025

Publication Date

6-30-2017

Comments

Open Access, Green

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