Classifying Fonts and Calligraphy Styles Using Complex Wavelet Transform

TL;DR

This paper presents a novel, language-independent method for font and calligraphy style recognition using complex wavelet transform features and support vector machines, demonstrating high accuracy across multiple datasets.

Contribution

It introduces a new texture analysis approach for font and calligraphy classification that is both accurate and computationally efficient, applicable across different languages and scripts.

Findings

Achieves higher recognition accuracy than existing methods.

Effective in categorizing Ottoman calligraphy styles.

Applicable to multiple languages and scripts.

Abstract

Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases. A generic font-recognition system independent of language, script and content is desirable for processing various types of documents. At the same time, categorizing calligraphy styles in handwritten manuscripts is important for palaeographic analysis, but has not been studied sufficiently in the literature. We address the font-recognition problem as analysis and categorization of textures. We extract features using complex wavelet transform and use support vector machines for classification. Extensive experimental evaluations on different datasets in four languages and comparisons with state-of-the-art studies show that our proposed method achieves higher recognition accuracy while being computationally simpler. Furthermore, on a new dataset generated from Ottoman manuscripts, we show that the proposed method can also be used for categorizing Ottoman calligraphy with high accuracy.