8. A method for handling a received document, said received document having been transmitted electronically and involving an encoding scheme, the method comprising: ascertaining, using identification rules, whether a charset employed to encode said target document belongs to an excluded charset group, said excluded charset group having at least two charsets, each charset in said excluded charset group selected based on charset inherent characteristics; if said charset employed to encode said target document does not belong to said excluded charset group, performing processing of said received document by performing the steps below; receiving a plurality of text document samples, said plurality of text document samples being encoded with different encoding schemes and selected for training purposes, said different encoding schemes pertaining to charset encoding; training, using said plurality of text document samples, to obtain a set of machine learning models, said training including generating fundamental units from said plurality of text document samples for charsets of said plurality of text document samples, extracting a subset of said fundamental units as feature lists, said extracting said subset of said fundamental units including filtering said fundamental units to obtain fundamental units that are more discriminatory in describing differences between said different encoding schemes, converting said feature lists into a set of feature vectors, said feature vectors are grouped by charsets, and generating said set of machine learning models from said set of feature vectors using a SVM (Support Vector Machine) technique; applying said set of machine learning models against a set of target document feature vectors converted from said received document, said applying including analyzing said set of received document feature vectors using said set of machine learning models to compute similarity indicia between said set of target document feature vectors and said set of machine learning models associated with said different encoding schemes, wherein a first encoding scheme associated with said set of machine learning models is designated as said encoding scheme if characteristics of said first encoding scheme as represented by said set of machine learning models are computed to be most similar, relative to other encoding schemes of said different encoding schemes, to said set of received document feature vectors; and decoding said received document to obtain decoded content of said received document based on at least said first encoding scheme.
8. A method for handling a received document, said received document having been transmitted electronically and involving an encoding scheme, the method comprising: ascertaining, using identification rules, whether a charset employed to encode said target document belongs to an excluded charset group, said excluded charset group having at least two charsets, each charset in said excluded charset group selected based on charset inherent characteristics; if said charset employed to encode said target document does not belong to said excluded charset group, performing processing of said received document by performing the steps below; receiving a plurality of text document samples, said plurality of text document samples being encoded with different encoding schemes and selected for training purposes, said different encoding schemes pertaining to charset encoding; training, using said plurality of text document samples, to obtain a set of machine learning models, said training including generating fundamental units from said plurality of text document samples for charsets of said plurality of text document samples, extracting a subset of said fundamental units as feature lists, said extracting said subset of said fundamental units including filtering said fundamental units to obtain fundamental units that are more discriminatory in describing differences between said different encoding schemes, converting said feature lists into a set of feature vectors, said feature vectors are grouped by charsets, and generating said set of machine learning models from said set of feature vectors using a SVM (Support Vector Machine) technique; applying said set of machine learning models against a set of target document feature vectors converted from said received document, said applying including analyzing said set of received document feature vectors using said set of machine learning models to compute similarity indicia between said set of target document feature vectors and said set of machine learning models associated with said different encoding schemes, wherein a first encoding scheme associated with said set of machine learning models is designated as said encoding scheme if characteristics of said first encoding scheme as represented by said set of machine learning models are computed to be most similar, relative to other encoding schemes of said different encoding schemes, to said set of received document feature vectors; and decoding said received document to obtain decoded content of said received document based on at least said first encoding scheme. 11. The method of claim 8 wherein said set of feature vectors are converted using a TF-IDF (Term Frequency-Inverse Document Frequency) technique.