A Framework for Spelling Correction in Persian Language Using Noisy Channel Model

There are several methods offered for spelling correction in Farsi (Persian) Language. Unfortunately no powerful framework has been implemented because of lack of a comprehensive corpus. In this research a comprehensive corpus of Farsi errors is collected and a framework is presented for spelling correction of Farsi text. The framework consists of two main parts: error detection and error correction. In error detection part, after finding erroneous words in sample text, our program proposes some candidates for related correction. The paper focuses on describing the method of ranking related corrections. This method is customized version of Noisy Channel Spelling Correction for Farsi. This ranking method attempts to find intended correction c from a typo t, that maximizes P(c) P(t | c). In this paper different methods are described and analyzed to obtain a wide overview of the field. Our evaluation results show that Noisy Channel Model using our corpus and training set in this framework works more accurately and improves efficiently in comparison with other methods.