The language for which the greatest number of trigram matches were obtained, and for which the frequencies of occurrence of the trigrams exceeded a language-specific threshold, was chosen the winner. No results were specified. Their experiments revealed that the HMMs had better entropy than bigrams but were comparable to the computationally expensive trigrams. Judging by the results, it appears that language ID from character codes is a less hard problem than that from speech input. However, for OCR it is desirable to know the language of the document before trying the decoding.
More recent techniques try to determine the language of the text before doing the conversions.
Thus characters with ascenders are represented differently from those with descenders and in turn from those which are entirely contained between the baseline and x-line. A total of 8 CSCs represent the 52 basic characters and their diacritic forms. On the basis of different agglomerations of CSCs, a number of techniques for determining the language of a document have been developed. An investigation of these differences with a view to incorporating them into current systems is essential.