The State of the Art Recognize in Arabic Script through Combination of Online and Offline

TL;DR

This paper presents a combined online and offline Arabic handwriting recognition system using Hidden Markov Models and sequence alignment, achieving a 3% performance improvement over individual recognizers.

Contribution

It introduces a novel multi-classifier system that integrates online and offline recognition methods for Arabic script, enhancing recognition accuracy.

Findings

System improves recognition accuracy by about 3%.

Combining recognizers enhances performance over single systems.

Uses Hidden Markov Models with different preprocessing techniques.

Abstract

Handwriting recognition refers to the identification of written characters. Handwriting recognition has become an acute research area in recent years for the ease of access of computer science. In this paper primarily discussed On-line and Off-line handwriting recognition methods for Arabic words which are often used among then across the Middle East and North Africa People. Arabic word online handwriting recognition is a very challenging task due to its cursive nature. Because of the characteristic of the whole body of the Arabic script, namely connectivity between the characters, thereby the segmentation of An Arabic script is very difficult. In this paper we introduced an Arabic script multiple classifier system for recognizing notes written on a Starboard. This Arabic script multiple classifier system combines one off-line and on-line handwriting recognition systems. The Arabic script recognizers are all based on Hidden Markov Models but vary in the way of preprocessing and normalization. To combine the Arabic script output sequences of the recognizers, we incrementally align the word sequences using a norm string matching algorithm. The Arabic script combination we could increase the system performance over the excellent character recognizer by about 3%. The proposed technique is also the necessary step towards character recognition, person identification, personality determination where input data is processed from all perspectives.