Open-vocabulary recognition of machine-printed Arabic text using hidden Markov models

In this paper, we present multi-font printed Arabic text recognition using hidden Markov models (HMMs). We propose a novel approach to the sliding window technique for feature extraction. The size and position of the cells of the sliding window adapt to the writing line of Arabic text and ink-pixel distributions. We employ a two-step approach for mixed-font text recognition, in which the input text line image is associated with the closest known font in the first step, using simple and effective features for font identification. The text line is subsequently recognized by the recognizer that was trained for the particular font in the next step. This approach proves to be more effective than text recognition using a recognizer trained on samples from multiple fonts. We also present a framework for the recognition of unseen fonts, which employs font association and HMM adaptation techniques. Experiments were conducted using two separate databases of printed Arabic text to demonstrate the effectiveness of the presented techniques. The presented techniques can be easily adapted to other scripts, such as Roman script.