Handwriting synthesis is the problem of generating data close to how a human would write the text. The characterstics of the generated data could be that of specific writer or that from a generic model. Systhesis of handwriting pose a challange as writer spcecific features need to captured and preserved, yet at the same time, variability between handwriting should also be taken into account. Even with the given model, synthesis should not be deterministic since the variation that are found in human handwriting are stochastic.

Application of handwriting synthesis, includes, automatically creation of personalized handwritten documents, large amount of annotated handwritten data for training of recognition engines and writer independent matching and retrieval of handwritten documents.

For synthesis of Indic scripts, we model the handwriting at two levels. A stroke level model is used to capture the writing style and hand movements of the individual strokes. A space-time layout model is then used to arrange the synthesized strokes to form the words. Both the stroke model and the layout model can be learned from examples, and the method can learn from a single example, as well as a large collection to capture the variations. The model also allows us to synthesize the words in multiple Indic scripts through transliteration.

Annotation of handwriting is the process of labeling input data for training for the purpose of a variety of handwriting analysis problems like Handwriting recognition and writer identification systems. However manual annotation of large datasets is tedious, expensive, and error prone process, especially at character and stroke level. Lack of proper linguistic resources in form of annotated data sets is a major hurdle in building recognizers for them.

In many practical situations, plain transcripts of handwritten data is available, which can be used to make the process of annotation, easier. Data collection process can be carried out in different setting like, unrestricted data, designed text, dictation, and data generation (using handwriting synthesis). A parallel text is available in all the above case, except that of unrestricted data. For annotation, we use the model based handwriting synthesis unit described above to map the text corpora to handwriting space and annotation is propagated to word and character levels using elastic matching of handwriting. Stroke level annotation for online handwriting recognition is currently being done using semi-automatic tools.

• The primary unit of words is an akshara, which is a combination of multiple consonants, and ending in a vowel.
• Each language has a very large set of aksharas - usually multiple thousands
• Each akshara is composed of a single or multiple strokes, and no partial strokes.