Abstract: An Arabic spelling error detection and correction method for identifying real word spelling errors. The method uses a corpus of Arabic text alongside n-gram statistical techniques to detect erroneous words within the text. After identifying the erroneous word the method uses a dictionary formed from the corpus of Arabic text to retrieve candidate correction word to replace the erroneous word with. Using n-gram statistical models candidate correction words are generated and ranked in order of highest probable correction for the word. The generated and ranked correction words are assessed and the best correction word is selected. A final assessment of the correction is conducted and if the result is positive then erroneous word is replaced with the highest statistical correction.

Abstract: A method for Arabic and Farsi font recognition for determining the font of text using a nearest neighbor classifier, where the classifier uses a combination of features including: box counting dimension, center of gravity, the number of vertical and horizontal extrema, the number of black and white components, the smallest black component, the Log baseline position, concave curvature features, convex curvature features, direction and direction length features, Log-Gabor features, and segmented Log-Gabor features. The method is tested using various combination of features on various text fonts, sizes, and styles. It is observed the segmented Log-Gabor features produce a 99.85% font recognition rate, and the combination of all non-Log-Gabor features produces a 97.96% font recognition rate.