Holistic approach for classifying and retrieving personal Arabic handwritten documents

Authors:
Salama Brook;Zaher Al Aghbari
Affiliations:
Department of Computer Science, University of Sharjah, UAE;Department of Computer Science, University of Sharjah, UAE
Venue:
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
Year:
2008

Citing 4
Cited 3

Survey and bibliography of Arabic optical text recognition

Signal Processing
An Omnifont Open-Vocabulary OCR System for English and Arabic

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Holistic Paradigms in Handwritten Word Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Transcript Mapping for Historic Handwritten Document Images

IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)

Latent Style Model: Discovering writing styles for calligraphy works

Journal of Visual Communication and Image Representation
HAH manuscripts: A holistic paradigm for classifying and retrieving historical Arabic handwritten documents

Expert Systems with Applications: An International Journal
Robust news video text detection based on edges and line-deletion

WSEAS Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel holistic technique for classifying and retrieving Arabic handwritten text documents. The retrieval of Arabic handwritten documents is performed in several steps. First, the Arabic handwritten document images are segmented into words, and then each word is segmented into its connected parts. Second, several features are extracted from these connected parts and then combined to represent a word with one consolidated feature vector. Finally, a generalized feedforward neural network is used to learn and classify the different styles/fonts into word classes, which are used to retrieve Arabic handwritten text documents.