A Data Base for Arabic Handwritten Text Recognition Research

Authors:
Somaya Al-Ma'adeed;Dave Elliman;Colin A. Higgins
Affiliations:
-;-;-
Venue:
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Year:
2002

Citing 0
Cited 10

Offline Arabic Handwriting Recognition: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of off-line printed Arabic text using Hidden Markov Models

Signal Processing
A Database for Arabic Printed Character Recognition

ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
A novel minimal Arabic script for preparing databases and benchmarks for Arabic text recognition research

WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters

Pattern Recognition
A database for offline arabic handwritten text recognition

ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Creating a handwriting recognition corpus for Bushman languages

ICADL'11 Proceedings of the 13th international conference on Asia-pacific digital libraries: for cultural heritage, knowledge dissemination, and future creation
Text-dependent writer identification for Arabic handwriting

Journal of Electrical and Computer Engineering
Offline arabic handwritten text recognition: A Survey

ACM Computing Surveys (CSUR)
KHATT: An open Arabic offline handwritten text database

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new database for offline Arabic handwriting recognition, together with associated preprocessing procedures. We have developed a new database for the collection, storage and retrieval of Arabic handwritten text (AHDB). This is an advance bothin terms of the size of the database as well as the number of different writers involved. We further designed an innovative, simple yet powerful, in place tagging procedure for our database. It enables us to easily extract the bitmaps of words. We also constructed apreprocessing class, which contains some useful preprocessing operations. In this paper the most popular words in Arabic writing were identified for the first time, using an associated program.