6,903 research outputs found
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
We address the design of a unified multilingual system for handwriting
recognition. Most of multi- lingual systems rests on specialized models that
are trained on a single language and one of them is selected at test time.
While some recognition systems are based on a unified optical model, dealing
with a unified language model remains a major issue, as traditional language
models are generally trained on corpora composed of large word lexicons per
language. Here, we bring a solution by con- sidering language models based on
sub-lexical units, called multigrams. Dealing with multigrams strongly reduces
the lexicon size and thus decreases the language model complexity. This makes
pos- sible the design of an end-to-end unified multilingual recognition system
where both a single optical model and a single language model are trained on
all the languages. We discuss the impact of the language unification on each
model and show that our system reaches state-of-the-art methods perfor- mance
with a strong reduction of the complexity.Comment: preprin
On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
On-line handwritten scripts are usually dealt with pen
tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this
paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples
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