Categorization of Various Essential Datasets and Methods for Textual Spelling Detection and Normalization

One of the most primary phases of automatic text processing is spelling error detection and grapheme normalization. Storing textual documents faces several problems without passing this phase, which causes a disturbance in retrieving the documents automatically. Therefore, specialists in the fields of natural language processing and computational linguistics usually make an attempt to sample various data through presenting ideal methods and algorithms in order to reach the normalized data. Several researches have been conducted on English and some other languages, which have been followed by a certain amount of researches on Farsi too. Sometimes, these several researches have remained to be a pure study and sometimes they have been released as a product. This paper carries out the categorization of the different methods and essential datasets in these researches and depicts each category individually and the evaluation measurements methods generally. Moreover, it describes the performance of the monolingual Farsi systems and the way they meet the Farsi challenges.