Classifying Fonts and Calligraphy Styles Using Complex Wavelet Transform
This addresses the problem of font and calligraphy classification for document analysis and palaeography, but it is incremental as it applies existing techniques to new domains.
The paper tackles font and calligraphy style recognition by treating it as texture analysis, using complex wavelet transform and support vector machines. It achieves higher recognition accuracy compared to state-of-the-art methods on datasets in four languages and demonstrates high accuracy on a new Ottoman calligraphy dataset.
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.