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Devanagari is a script used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people. Unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, direction and shape of the constituent strokes. An on-line pen computing environment has numerous application in providing an easy human interjiace for a complex script like Devanagari. A Devanagari character recognition experiment with 20 different writers with each writer writing 5 samples of each character in a totally unconstrained way, has been conducted. An accuracy of 86.5% with no rejects is achieved through the combination of multiple classifiers that focus on either local on-line properties, or global off-line properties. Further improvements in performance are expected by using word-level contextual information. We are also exploring the use of writer dependent models to improve the recognition accuracy.

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Complex predicate is a noun, a verb, an adjective or an adverb followed by a light verb that behaves as a single unit of verb. Complex predicates (CPs) are abundantly used in Hindi and other languages of Indo Aryan family. Detecting and interpreting CPs constitute an important and somewhat a difficult task. The linguistic and statistical methods have yielded limited success in mining this data. In this paper, we present a simple method for detecting CPs of all kinds using a Hindi-English parallel corpus. A CP is hypothesized by detecting absence of the conventional meaning of the light verb in the aligned English sentence. This simple strategy exploits the fact that CP is a multiword expression with a meaning that is distinct from the meaning of the light verb. Although there are several shortcomings in the methodology, this empirical method surprisingly yields mining of CPs with an average precision of 89% and a recall of 90%.