Turkish handwritten text recognition: a case of agglutinative languages

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate

RAPID ANALYTICAL VERIFICATION OF HANDWRITTEN ALPHANUMERIC ADDRESS FIELDS

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF This paper presents a combination of fuzzy system and dynamic analytical model to deal with imprecise data derived from feature extraction in handwritten address images which are compared against postulated addresses for address verification. A dynamic building­number locator is able to locate and recognise the building­number, without knowing exactly where the building­number starts in the candidate address line. The overall system achieved a correct sorting rate of 72.9%, 27.1% rejection rate and 0.0% error rate on a blind test set of 450 cursive handwritten addresses.

Comparison of crisp and fuzzy character networks in handwritten word recognition

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level