Classifying pages or text lines into font categories aids transcription because single font Optical Character Recognition (OCR) is generally more accurate than omni-font OCR. We present a simple framework based on Convolutional Neural Networks (CNNs), where a CNN is trained to classify small patches of text into predefined font classes. To classify page or line images, we average the CNN predictions over densely extracted patches. We show that this method achieves state-of-the-art performance on a challenging dataset of 40 Arabic computer fonts with 98.8 line level accuracy. This same method also achieves the highest reported accuracy of 86.6 in predicting paleographic scribal script classes at the page level on medieval Latin manuscripts. Finally, we analyze what features are learned by the CNN on Latin manuscripts and find evidence that the CNN is learning both the defining morphological differences between scribal script classes as well as overfitting to class-correlated nuisance factors. We propose a novel form of data augmentation that improves robustness to text darkness, further increasing classification performance.
Zramdini and Ingold presented a font recognition system based on the statistics of connected components, achieving 97.35 posed font recognition as texture identification and used Gabor Filters to achieve 99.1 Dimension features were introduced in @cite_10 for Arabic font classification and resulted in 98 used log-Gabor filter features extracted at multiple scales and orientations to obtain 96.1 More recently, deep learning techniques based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been proposed for font classification. used a combination of CNN and 2D RNN models to classify single Chinese characters into 7 font classes with 97.77 classified handwritten Chinese characters into 5 calligraphy classes with 95 calligraphy classes is similar to classifying script types in CLaMM, though CLaMM uses Latin script classes with page-level ground truth.
@cite_10
1991904384
In this work, a new method is proposed to the widely neglected problem of Arabic font recognition, it uses global texture analysis. This method is based on fractal geometry, and the feature extraction does not depend on the document contents. In our method, we take the document as an image containing some specific textures and regard font recognition as texture identification. We have combined both techniques BCD (box counting dimension) and DCD (dilation counting dimension) to obtain the main features. The first expresses texture distribution in 2-D image. The second makes possible to take on the human vision system aspect, since it makes it possible to differentiate one font from another. Both features are expressed in a parametric form; then four features were kept. Experiments are carried out by using 1000 samples of 10 typefaces (each typeface is combined with four sizes). The average recognition rates are of about 96.2 using KNN (K nearest neighbor) and 98 using RBF (radial basic function). Experimental results are also included in the robustness of the method against written size, skew, image degradation (e.g., Gaussian noise) and resolution, and compared with the existing methods. The main advantages of our method are that (1) the dimension of feature vector is very low; (2) the variation sizes of the studied blocks (which are not standardized) are robust; (3) less samples are needed to train the classifier; (4) finally and the most important, is the first attempt to apply and adapt fractal dimensions to font recognition.