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Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.


Papers
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Proceedings ArticleDOI
24 Aug 2013
TL;DR: This research paper proposes a recognition system for handwritten Devanagari Compound character recognition based on Legendre moment feature descriptor, which has been successfully applied to many pattern recognition problem.
Abstract: Handwritten Devanagari Compound character recognition is one of the new challenging task for the researcher, because Compound character are complex in structure, they are written by combination two or more character. Their occurrence in the script is up to 12 to 15%. In this research paper, a recognition system for handwritten Devanagari Compound Character is proposed bases on Legendre moment feature descriptor are used to recognize. Moment function have been successfully applied to many pattern recognition problem, due to this they tends to capture global features which makes them well suited as feature descriptor. The process image is normalized to 30X30 pixel size divided into zone, from this structural as well as statistical feature are extracted from each zone. The proposed system is trained and tested on 27000 handwritten collected from different people. For classification we have used Artificial Neural Network. The overall recognition rate for basic is up to 98.25% and for all compound character is 98.36%.

11 citations

Proceedings ArticleDOI
Kyung-Won Kang1, J.H. Kim1
03 Aug 2003
TL;DR: A stochastic modeling scheme by which strokes as well as relationships are represented by utilizing the hierarchical characteristics of target characters is proposed and a handwritten Hangul (Korean) character recognition system is developed.
Abstract: In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. In this paper, we propose a stochastic modeling scheme by which strokes as well as relationships are represented by utilizing the hierarchical characteristics of target characters. Based on the proposed scheme, a handwritten Hangul (Korean) character recognition system is developed. The effectiveness of the proposed scheme is shown through experimental results conducted on a public database.

11 citations

Proceedings ArticleDOI
01 Sep 2000
TL;DR: A robust multifont character recognition system for degraded documents, such as photocopy or fax, is described and clearly outperforms commercial systems and leads to further error rate reductions compared to previous results reached on this database.
Abstract: A robust multifont character recognition system for degraded documents, such as photocopy or fax, is described. The system is based on hidden Markov models using discrete and hybrid modeling techniques, where the latter makes use of an information theory-based neural network. The presented recognition results refer to the SEDAL-database of English documents using no dictionary. It is also demonstrated that the usage of a language model that consists of character n-grams yields significantly better recognition results. Our resulting system clearly outperforms commercial systems and leads to further error rate reductions compared to previous results reached on this database.

11 citations

Proceedings ArticleDOI
Marc-Peter Schambach1
26 Jul 2009
TL;DR: For a standard HMM-based word recognition system, a new recurrent HMM approach for very fast lexicon-free recognition will be presented, which allows fast evaluation of word hypotheses, easy integration of various language models like n-grams, and the efficient extraction of lexicons-free n-best result alternatives.
Abstract: Standard cursive handwriting recognition is based on a language model, mostly a lexicon of possible word hypotheses or character n-grams. The result is a list of word alternatives ranked by confidence. Present-day applications use very large language models, leading to high computational costs and reduced accuracy. For a standard HMM-based word recognition system, a new recurrent HMM approach for very fast lexicon-free recognition will be presented. The evaluation of this model creates a "recognition graph", a compact representation of result alternatives of lexicon-free recognition. This structure is formally identical to results of single character segmentation and recognition. Thus it can be directly evaluated by interpretation algorithms following this process, and can even be merged with these results. In addition, the recognition graph is a basis for further evaluation in terms of word recognition. It allows fast evaluation of word hypotheses, easy integration of various language models like n-grams, and the efficient extraction of lexicon-free n-best result alternatives.

11 citations

Proceedings ArticleDOI
22 Oct 1995
TL;DR: This paper described techniques for handwritten text recognition to enable recognition of ordinary handwritten text in documents and demonstrated sentence level recognition of /spl ges/63% (top choice) on a limited data set.
Abstract: This paper describes techniques for handwritten text recognition to enable recognition of ordinary handwritten text in documents. The various components of a handwritten text recognition system are described. Sentence level recognition of /spl ges/63% (top choice) is demonstrated on a limited data set.

11 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202314
202241
20201
20192
20189
201751