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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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BookDOI
01 Jan 1994
TL;DR: This paper presents a meta-modelling approach for practical character recognition system development using a pen-based music editor and a model-based dynamic signature verification system.
Abstract: 1: Introduction and overview of field.- Frontiers in handwriting recognition.- 2: Handwritten character recognition.- Historical review of theory and practice of handwritten character recognition.- Automatic recognition of handwritten characters.- Learning, representation, understanding and recognition of characters and words - an intelligent approach.- Digital transforms in handwriting recognition.- Pattern recognition with optimal margin classifiers.- 3: Handwritten word recognition.- On the robustness of recognition of degraded line images.- Invariant handwriting features useful in cursive script recognition.- Off-line recognition of bad quality handwritten words using prototypes.- Handwriting recognition by statistical methods.- Towards a visual recognition of cursive script.- A hierarchical handwritten word segmentation.- 4: Contextual methods in handwriting recognition.- Cursive words recognition: methods and strategies.- Hidden Markov models in handwriting recognition.- Language-level syntactic and semantic constraints applied to visual word recognition.- Verification of handwritten British postcodes using address features.- Improvement of OCR by language model.- An approximate string matching method for handwriting recognition post-processing using a dictionary.- 5: Neural networks in handwriting recognition.- Neural-net computing for machine recognition of handwritten English language text.- Cooperation of feedforward neural networks for handwritten digit recognition.- Normalisation and preprocessing for a recurrent network off-line handwriting recognition system.- 6: Architectures for handwriting.- Architectures for handwriting recognition.- 7: Databases for handwriting recognition.- Large database organization for document images.- 8: Signature recognition and verification.- A model-based dynamic signature verification system.- Algorithms for signature verification.- Handwritten signature verification: a global approach.- 9: Application of handwriting recognition.- Total approach for practical character recognition system development.- A pen-based music editor.

53 citations

Proceedings ArticleDOI
23 Sep 2007
TL;DR: An online handwritten Japanese character string recognition system integrating scores of geometric context, character recognition, and linguistic context is described and a statistical method for modeling both single- character and between-character plausibility is proposed.
Abstract: This paper describes an online handwritten Japanese character string recognition system integrating scores of geometric context, character recognition, and linguistic context. We give a string evaluation criterion for better integrating the multiple scores while overcoming the effect of string length variability. For measuring geometric context, we propose a statistical method for modeling both single- character and between-character plausibility. Our experimental results on TUAT HANDS databases show that the geometric context improves the character segmentation accuracy remarkably.

53 citations

Proceedings ArticleDOI
06 Dec 2010
TL;DR: This article reported the results of online and offline handwritten Chinese character recognition using the new generation of databases, targeting 3,755 Chinese characters of the GB2312-80 first level set.
Abstract: Chinese handwriting recognition remains a challenge. Research works have reported very high accuracies on neatly handwritten characters yet the performance on unconstrained handwriting remains quite low. To promote the recognition technology, new databases of unconstrained handwriting have been constructed for academic research and benchmarking. This paper reports the contest results of online and offline handwritten Chinese character recognition using the new generation of databases, targeting 3,755 Chinese characters of the GB2312-80 first level set. Nine systems from four groups were submitted for evaluation. The best results are 92.39% accuracy for online character recognition and 89.99% accuracy for offline character recognition. Detailed analysis of results on data of different writers reveals the diversity of writing quality. The future contests will consider continuous script recognition as well as isolated character recognition.

53 citations

Journal ArticleDOI
TL;DR: The paper presents a survey of applications of OCR in different fields and further presents the experimentation for three important applications such as Captcha, Institutional Repository and Optical Music Character Recognition.
Abstract: Optical Character Recognition or OCR is the electronic translation of handwritten, typewritten or printed text into machine translated images. It is widely used to recognize and search text from electronic documents or to publish the text on a website. The paper presents a survey of applications of OCR in different fields and further presents the experimentation for three important applications such as Captcha, Institutional Repository and Optical Music Character Recognition. We make use of an enhanced image segmentation algorithm based on histogram equalization using genetic algorithms for optical character recognition. The paper will act as a good literature survey for researchers starting to work in the field of optical character recognition.

52 citations

Proceedings ArticleDOI
31 Aug 2005
TL;DR: A camera based optical character reader for Japanese Kanji characters was implemented on a mobile phone and recognition accuracy of over 95% was obtained under the best conditions, which shows the potential of the prototype as a new type of electronic dictionary.
Abstract: A camera based optical character reader (OCR) for Japanese Kanji characters was implemented on a mobile phone. This OCR has three key features. The first is discriminative feature extraction (DFE) which enables a character classifier needing only small memory size. The second is a word segmentation method specially designed for looking up Japanese words in a dictionary. The third feature is a GUI suitable for a mobile phone. A prototype mobile phone Kanji OCR was constructed and experimentally tested. Recognition accuracy of over 95% was obtained under the best conditions, which shows the potential of our prototype as a new type of electronic dictionary.

52 citations


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