Design and implementation of Context-Based Language Spell Checker For Handheld Device
ABSTRACT
Spellchecking is a spelling check app that will carefully go through your text to scan it for any spelling errors and correct them by providing possible ranked suggestions for users to select from a list and fix misspelled words. This thesis describes the design architecture, implementation, and testing of a model that has been developed by the programing language Python. This spellchecker came with an integrated user-friendly graphical user interface, where users can input their text, detect misspelled words and choose from a list of five candidate correction words to correct them. Users can even add words to a pre-built dictionary. Error detection is based on the dictionary look-up method, bigram, and trigram analysis. The data was collected from different scientific and error-free as well as trusted sources and prepared the dictionary, bigram, and trigram models for error detection and correction. Two types of error happened in the spelling check system to detect and correct both context-aware/ real word and non-word error types. The main focus of this study is to design context-based spell checkers for Afan Oromo language hand-held devices depending on the spelling error patterns of language based on the sequence of words in the input sentences contextually. The first type of spelling error is non-word error candidate generation is based on dictionary lookup techniques, similarity is measured using the Levenshtein edit distance by considering the Insertion, deletion, substitution, and transposition of the character of user input to the dictionary token and ranking top 5 probable suggestions accordingly. The second type of error occurs during spell check that is the real word error, for this type of error, the bigram and trigram model created from the corpus and Stord based on statically/probabilistic analysis techniques was used to identify the misspelled word based on context to correct bad word according to context misspelled. To conduct the experiment 1500 words were used to learn and test the model respectively. The experiment result shows that, an accuracy of 85% for spelling errors. According to the gated result, the accuracy of the system is 85%, this shows that the model is convenient and efficient in order to correct misspelling words in both real word and word types of spell error occurred while user type texts to communicate.
Design and implementation of Context-Based Language Spell Checker For Handheld Device, GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS