This paper discusses a new approach for Transcription Cataloging (TC) based on combining efficient algorithms. The important aspect of automatically sorting and classifying a set of documents into any category by incorporating a predefined set is Transcription Cataloging. Automated Transcription Cataloging is gaining notability since it frees organizations from the hectic and time consuming need of manually organizing documents, which can be too expensive, or simply not feasible given the time constraints of the application or the number of documents involved. In terms of accuracy, modern Transcription Cataloging systems proves better than that of trained human professionals, which is made possible by a combination of information retrieval technology and machine learning technology in Transcription Cataloging approach. There are numerable useful applications of this approach spanning various scientific and general fields of work. This paper deals in depth the feasibility of Transcription Cataloging pertaining to various domains along with making substantial use of techniques like document indexing, text filtering and classifier learning technique. Also the approaches of standard input and tokenization are considered for a better output which shall be devoid of any complexity for Transcription Cataloging.

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Transcription cataloging Fast-KNN & Naïve Bayesian algorithms pair-relation deductive inference