9,193 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
Query by String word spotting based on character bi-gram indexing
In this paper we propose a segmentation-free query by string word spotting
method. Both the documents and query strings are encoded using a recently
proposed word representa- tion that projects images and strings into a common
atribute space based on a pyramidal histogram of characters(PHOC). These
attribute models are learned using linear SVMs over the Fisher Vector
representation of the images along with the PHOC labels of the corresponding
strings. In order to search through the whole page, document regions are
indexed per character bi- gram using a similar attribute representation. On top
of that, we propose an integral image representation of the document using a
simplified version of the attribute model for efficient computation. Finally we
introduce a re-ranking step in order to boost retrieval performance. We show
state-of-the-art results for segmentation-free query by string word spotting in
single-writer and multi-writer standard datasetsComment: To be published in ICDAR201
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
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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