Integration of Gabor Features with Bag-of-Features Framework for Arabic Handwritten Word Recognition

Handwriting recognition is a challenge task due to the large variability in human writings. Improving the feature representations that rely on the visual appearance of the handwritten text would lead to better recognition. In this paper, we integrate two powerful appearance-based features for producing robust statistics for handwritten text. A handwritten text image is filtered by a set of Gabor filters of different scales and orientations for extracting texture-based local features. The Gabor filter response features are organized into two layouts, viz. the Statistical Gabor Features and Gabor Descriptors, and fed to the Bag-of-Features for learning robust statistical representations for the handwritten text. The produced features are utilized in a holistic handwritten word recognition system and evaluated on a public dataset of Arabic handwritten subwords of Arabic checks legal amounts. 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.