1. An Arabic optical character recognition method using Hidden Markov Models and decision trees, comprising steps of: receiving an input image containing an Arabic text; removing all diacritics from said input image by detecting a bounding box of each diacritic and comparing coordinates thereof with those of a bounding box of a text body; segmenting said input image into four layers, and conducting feature extraction on the segmented four layers; inputting results of feature extraction into a Hidden Markov Model thereby generating HMM models for representing each Arabic character; conducting iterative training of said HMM models until an overall likelihood criterion is satisfied; and inputting results of iterative training into a decision tree thereby predicting locations and the classes of said diacritics and producing final recognition results.