Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

Recognition of handwritten Arabic cursive texts is a complex task due to the similarities between letters under different writing styles. In this paper, a word-based off-line recognition system is proposed, using Hidden Markov Models (HMMs). The method employed involves three stages, namely preprocessing, feature extraction and classification. First, words from input scripts are segmented and normalized. Then, a set of intensity features are extracted from each of the segmented words, which is based on a sliding window moving across each mirrored word image. Meanwhile, structure-like features are also extracted including number of subwords and diacritical marks. Finally, these features are applied in a combined scheme for classification. Intensity features are used to train a HMM classifier, whose results are re-ranked using structure-like features for improved recognition rate. In order to validate the proposed techniques, extensive experiments were carried out using the IFN/ENIT database which contains 32,492 handwritten Arabic words. The proposed algorithm yields superior results of improved accuracy in comparison with several typical methods.

Research highlights
► To design efficient pre-processing for baseline detection and word.
► To extract several structural features and a group of intensity features using a sliding window.
► To combine intensity-feature-based HMM and structure-feature re-ranking for word recognition.
► A quantitative measurement to indicate how biased a test set is distributed for error analysis.