SweetCoat-2D: Two-Dimensional Bangla Spelling Correction and Suggestion Using Levenshtein Edit Distance and String Matching Algorithm

Autonomous spelling correction and suggestion generation for any language is a tough task since the system must grasp context or it will create a negative impression. Our goal is to reduce this difficulty and provide a more accurate spelling check and suggestions for the Bangla language. Recently, the Bangla language has entered a more advanced phase of its procedure for automatically repairing misspelled words. However, just a few works have been undertaken on this subject that is adequate. In this study, we have developed an English-coated two-dimensional (2D) encoding mechanism in terms of Bangla Grammer Book, dubbed SweetCoat-2D that is utilized in conjunction with Levenshtein Edit Distance and several String matching approaches to evaluate more precisely. Our encoding method outperforms the rest of the process, thus the outcomes are encouraging. Also, we have constructed a corpus of 15,000 misspelled words to which we apply our SweetCoat-2D encoding algorithm in conjunction with the Levenshtein Distance and String Matching algorithms, yielding improved spelling suggestion results of 92.404%, where accuracy is calculated only for correct suggestions which are provided at the first index. Therefore, there is a high probability that the presented model will be useful and have a substantial impact on the process of correcting misspellings in Bangla.