Spelling Check Combined Language Models and Knowledge Resources for Printer Drivers

Abstract

This paper proposed a spelling error detection and correction using the linguistic features and knowledge resource. The linguistic features mainly come from language model that describes the probability of a sentence. In practice, the formal document with typos is defective and fall short of the specifications, since typos and error hidden in printed document are frequent, rework will cause the waste of paper and ink. This paper proposed an approach that addresses the spelling errors and before printing. In this method, the linguistic features are used in this research to compare and increase a new feature additionally that is a function of Internet search based on knowledge bases. Combining these research manners, this paper expect to achieve the goals of confirming, improving the detection rate of typos, and reducing the waste of resources. Experimental results shows, the proposed method is practicable and efficient for users to detect the typos in the printed documents.