Topic
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 published on a yearly basis
Papers
More filters
TL;DR: The novelty of the approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components.
35 citations
Book•
01 Nov 1999
TL;DR: This paper presents a meta-modelling architecture suitable for pattern recognition and machine intelligence that has been developed at the university level and at the national and international level.
Abstract: 1 Centre for Pattern Recognition and Machine Intelligence Department of Computer Science, Concordia University 1455 de Maisonneuve Boulevard West Suite GM-606, Montreal, Canada H3G 1M8 2 Ecole de Technologie Superieure Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA) 1100 Notre-Dame Ouest, Montreal, Canada H3C 1K3 3 Service de Recherche Technique de La Poste Departement Reconnaissance, Modelisation Optimisation (RMO) 10, rue de l’ile Mâbon, 44063 Nantes Cedex 02, France 4 Departamento de Informatica (Computer Science Department) Pontificia Universidade Catolica do Parana Av. Imaculada Conceicao, 1155 Prado Velho 80.215-901 Curitiba PR BRAZIL
35 citations
16 Jul 2008
TL;DR: The projection distance metric and zoning based scheme for numeral recognition and a nearest neighbor classifier is used for subsequent purpose and gives around 93% and 90% of recognition accuracy for Kannada and Tamil numerals respectively.
Abstract: Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either online or off-line. There is a large demand for Optical character recognition on hand written documents. India is a multi-lingual country and multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we have proposed the projection distance metric and zoning based scheme for numeral recognition. We tested our proposed method for Kannada and Tamil numerals. A nearest neighbor classifier is used for subsequent purpose. The proposed method gives around 93% and 90% of recognition accuracy for Kannada and Tamil numerals respectively.
35 citations
Patent•
IBM1
TL;DR: In this paper, a handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word and a decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of handwritten word.
Abstract: A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word. Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.
35 citations
TL;DR: Tests carried out on a sample of 15 writers show the interest of the proposed adaptation scheme since they obtain during iterations an improvement of recognition rates both at the letter and the word levels.
35 citations