Automatic Correction of Text Using Probabilistic Error Approach
P. Nagaraj, Venkatkumar Muneeswaran, Niamatullah Ghous, Mahtab Ahmad, Pavan Kumar V, Vinayak Vinayak · 2023
Continuous research effort has been paid to automatic spelling correction. Although each study provides a quick introduction to the problem, there is a paucity of work that would combine the analytical foundations and provide a summary of the approaches investigated thus far. our study has chosen spelling correction publications from 1991 to 2019 that were indexed in Scopus and Web of Science. The first group follows a predetermined set of guidelines. The second group makes use of an extra context model. The third subset of the survey’s computerized spelling checkers can modify its model to fit the situation. The summary tables identify each system’s application domain, language, context model, and string metrics. To fix the publicly available construction data, this work attempts to create an automated data correction system. The handling of the construction data is hampered by its unstructured character. Due to different types of users, lengthy system operation, and substantial pretraining time, the information management system has a lot of data in inconsistent formats or even erroneous data. The conversion of the construction data into a machine-readable format requires a lot of time and work.