The most important model for written language recognition is the lexicon of words. The lexicon, in turn, is determined by linguistic constraints, e.g., in recognizing running text, the lexicon for each word is constrained by the syntax, semantics and pragmatics of the sentence. The performance of a recognition system can be improved by incorporating statistical information at the word sequence level. The performance improvement derives from selection of lower-rank words from the word recognition output when the surrounding context indicates such selection makes the entire sentence more probable. Lexical techniques such as collocational analysis can be used to modify word neighbourhoods generated by a word recognizer.
Modification includes re-ranking, deleting or proposing new word candidates.
An example of a handwritten sentence together with recognition choices produced by a word recognizer and grammatically determined correct paths are shown in Fig. Figure 2 Document Image Analysis Interactive with written language recognition is the task of document analysis. There is wide variability in the structure of documents, as in the case of newspapers, magazines, books, forms, letters and handwritten notes. Document analysis involves traditional image processing operations, such as enhancement, gray-scale image binarization, texture analysis, segmentation, etc. Conclusion Research on automated written language recognition dates back several decades.