Optical Font Recognition Using Typographical Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Automatic Detection of Italic, Bold and All-Capital Words in Document Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Gabor Filter Based Multi-class Classifier for Scanned Document Images
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Font Type Extraction and Character Prototyping Using Gabor Filters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
High-order statistical texture analysis--font recognition applied
Pattern Recognition Letters
Multi-Linguistic Optical Font Recognition Using Stroke Templates
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
An EMD-based recognition method for Chinese fonts and styles
Pattern Recognition Letters
A Novel Structure for Radial Basis Function Networks--WRBF
Neural Processing Letters
Pattern Recognition Letters
Arabic font recognition based on diacritics features
Pattern Recognition
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A new approach for the recognition of Farsi fonts is proposed. Font type of individual lines with any font size is recognized based on a new feature. Previous methods proposed for font recognition are mostly based on Gabor filters and recognize font type of a block of text rather than a line or a phrase. Usually all text lines of the same block or paragraph do not have the same font, e.g. titles usually have different fonts. On the other hand although the Gabor filter does this task fairly, but it is very time consuming, so that feature extraction of a texture of size 128*128 takes about 178ms on a 2.4GHz PC. In this paper we perform font recognition in line level using a new feature based on Sobel and Roberts gradients in 16 directions, called SRF. We break each line of text into several small parts and construct a texture. Then SRF is extracted as texture features for the recognition. This feature requires much less computation and therefore it can be extracted very faster than common textural features like Gabor filter, wavelet transform or momentum features. Our experiments show that it is about 50 times faster than an 8-channel Gabor filter. At the same time, SRF can represent the font characteristics very well, so that we achieved the recognition rate of 94.16% on a dataset of 10 popular Farsi fonts. This is about 14% better than what an 8-channel Gabor filter can perform. If we ignore the errors between very similar fonts, the recognition rate of about 96.5% will be achieved.