A prototype system for handwritten sub-word recognition: Toward Arabic-manuscript transliteration

TL;DR

This paper presents a prototype system for recognizing and transliterating sub-words in handwritten Arabic manuscripts, including dot-less scripts, using skeleton features and binary classifiers, with promising initial results.

Contribution

It introduces a novel approach combining skeleton features and binary classifiers for sub-word recognition in Arabic manuscripts, including archigraphemic scripts.

Findings

System successfully recognizes sub-words with promising accuracy.

Binary classifiers reduce recognition complexity effectively.

System adaptable to various Arabic manuscript styles.

Abstract

A prototype system for the transliteration of diacritics-less Arabic manuscripts at the sub-word or part of Arabic word (PAW) level is developed. The system is able to read sub-words of the input manuscript using a set of skeleton-based features. A variation of the system is also developed which reads archigraphemic Arabic manuscripts, which are dot-less, into archigraphemes transliteration. In order to reduce the complexity of the original highly multiclass problem of sub-word recognition, it is redefined into a set of binary descriptor classifiers. The outputs of trained binary classifiers are combined to generate the sequence of sub-word letters. SVMs are used to learn the binary classifiers. Two specific Arabic databases have been developed to train and test the system. One of them is a database of the Naskh style. The initial results are promising. The systems could be trained on other scripts found in Arabic manuscripts.