A Framework to Find Single Language Version Using Pattern Analysis in Mixed Script Queries

Pattern analysis for converting a complete mixed code into a single language after determining confidence levels is a systematic approach crucial for applications requiring language uniformity. The process begins by tokenizing the mixed code into individual words or segments and employing a language identification algorithm to assign potential languages with associated confidence scores to each segment. Establishment of a confidence threshold allows for the reliable selection of dominant languages within the mixed code. Segmentation based on confidence levels and the application of a majority language rule ensures the prioritization of the most reliable language segments. In cases where confidence falls below the threshold or ambiguity arises, a fallback mechanism can be implemented, offering options like defaulting to a predefined language or flagging for manual review. The conversion strategy is then formulated based on the identified dominant language, utilizing tools such as machine translation services or language-specific processing libraries. The final output is a unified, single-language version of the mixed code, providing consistency and reliability in diverse linguistic contexts. The bulk of the words are Hindi, it has been noted after handing to the model. This is the reason these mixed codes need to be translated into majority language script. Make a table of replacements to swap out the unclear acronym with the matching term. There are 79 words in all, and 1 of those words are erroneous; thus, the correctness of the work is $(\mathbf{78} / \mathbf{79}) \times \mathbf{100}=\mathbf{98.73} \%$, and the mistake rate is $(\mathbf{1}/\mathbf{79})\times \mathbf{100}=\mathbf{1.27}\%$.