Recognition of Arabic handwritten words using gabor-based bag-of-features framework

Holistic recognition of isolated words is an essential task in several daily life applications, e.g., bank check processing and postal address reading. In this work we present a system for the automatic recognition of Arabic handwritten words based on statistical features extracted by Bag-of-Features framework that exploits the discriminative power of Gabor features. A handwritten text image is filtered by a set of Gabor filters of different scales and orientations for extracting texture-based local features. The response of the Gabor filters are organized into two layouts, viz. the Statistical Gabor Features and Gabor Descriptors, and fed to the Bag-of-Features in order to produce statistical representations for the handwritten text. The produced features are utilized in a holistic handwritten word recognition system that is applied on handwritten Arabic checks legal amounts public dataset. The effective parameters of the two layouts as well as the Bag-of-Features framework are experimentally evaluated and the optimal values are used in reporting the final recognition accuracies. The best average recognition accuracy achieved by the produced features is 86.44% which is promising in such challenge dataset of large number of classes.