2014/07/09 by Alican Bozkurt, Bozkurt, Alican, Pınar Duygulu +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Currency Recognition and Detection #Digital Media Forensic Detection #FOS: Computer and information sciences #Handwritten Text Recognition Techniques
paper · pdf · doi:10.48550/arxiv.1407.2649
openalex publication_date 2014/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.