3. The method of claim 1 , further comprising: building an Arabic corpus comprising Arabic text files and ground truths corresponding to each of the Arabic text files, wherein the Arabic text files include Arabic texts written in different writing styles; and storing writing-style indices in association with the Arabic text files by a computer, wherein each of the writing-style indices indicates that one of the Arabic text files is written in one of the writing styles, wherein the Hidden Markov Model is trained using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices in association with the Arabic text files.
3. The method of claim 1 , further comprising: building an Arabic corpus comprising Arabic text files and ground truths corresponding to each of the Arabic text files, wherein the Arabic text files include Arabic texts written in different writing styles; and storing writing-style indices in association with the Arabic text files by a computer, wherein each of the writing-style indices indicates that one of the Arabic text files is written in one of the writing styles, wherein the Hidden Markov Model is trained using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices in association with the Arabic text files. 8. The method of claim 3 , wherein the step of segmenting one of the plurality of lines of Arabic text into Arabic words comprises: identifying two adjacent strings of Arabic characters as potential adjacent words; comparing a spacing between the two adjacent strings of Arabic characters to a predetermined threshold; and a word boundary is determined between two adjacent strings of Arabic characters when the spacing between the two adjacent strings of Arabic characters exceeds the predetermined threshold.