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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.


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TL;DR: New methods for handwritten Arabic character recognition which is based on novel preprocessing operations including different kinds of noise removal also different kind of features like structural, Statistical and Morphological features from the main body of the character and also from the secondary components are proposed.
Abstract: There are many difficulties facing a handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of distinct character shapes, interconnections of neighbouring characters and their position in the word. The typical Optical Character Recognition (OCR) systems are based mainly on three stages, preprocessing, features extraction and recognition. This paper proposes new methods for handwritten Arabic character recognition which is based on novel preprocessing operations including different kinds of noise removal also different kind of features like structural, Statistical and Morphological features from the main body of the character and also from the secondary components. Evaluation of the accuracy of the selected features is made. The system was trained and tested by back propagation neural network with CENPRMI dataset. The proposed algorithm obtained promising results as it is able to recognize 88% of our test set accurately. In Comparable with other related works we find that our result is the highest among other published works.

17 citations

Proceedings ArticleDOI
04 Nov 2010
TL;DR: A handwritten character recognition algorithm based on artificial immune that steals the merit of self-adaptive learning, and immune memory in the biology immune system, which can also be applied to abnormity detection and pattern recognition.
Abstract: Handwritten character recognition is an important research and application area on pattern recognition theory, which plays an important role on realizing automation of inputting character at all cases. In order to improve the rate of character recognition and decrease the time of recognition training, referencing to immune biological principle, a handwritten character recognition algorithm based on artificial immune is proposed. The antigen and memory cell in the artificial immune system are described. The equations of clone selection principle and of evolving memory cell are established. Finally, the process of character recognition is given. The experiment uses the well-know character set providing by F.Prat from UCI. The simulation results show that the method has faster speed and higher accuracy than the traditional handwritten recognition based on neural network. The algorithm steals the merit of self-adaptive learning, and immune memory in the biology immune system, which can also be applied to abnormity detection and pattern recognition.

17 citations

Proceedings ArticleDOI
14 Nov 1988
TL;DR: Preliminary results are presented to show how the initial stages of syntactic verification can improve character recognition performance.
Abstract: An optical character recognition (OCR) system is developed for recognizing handwritten and handprinted addresses which include a British postcode written within character boxes. The system makes use of syntactic information concerning postcodes and a postcode database which interacts with the character recognition process to ensure that only valid postcodes are recognized. Postulated valid postcodes are then verified using semantic features of the remainder of the address, to produce a final postcode which both matches the input characters and is compatible with the remainder of the address. Preliminary results are presented to show how the initial stages of syntactic verification can improve character recognition performance. >

17 citations

Proceedings ArticleDOI
01 Dec 2009
TL;DR: This work represents the development of an online handwriting recognition system for Bangla script, widely used in eastern India and Bangladesh, which is characterized by structure or shape based representation of a stroke in which a stroke is represented as a string of shape features.
Abstract: Developing efficient handwriting recognition systems that are fast and highly reliable is a challenging problem. This work represents the development of an online handwriting recognition system for Bangla script, widely used in eastern India and Bangladesh. In our approach, an online handwritten character/cluster is characterized by structure or shape based representation of a stroke in which a stroke is represented as a string of shape features. Using this string representation, an unknown stroke is identified by comparing it with a database of strokes using DTW (Dynamic Time Warping) technique. Identifying all the component strokes recognizes a full character. A recognition experiment has been conducted with a total of 495 classes on 20,873 data samples and 10 people as data contributors yielding 97.33% recognition rate with 2.18% misrecognition rate and 0.5% rejection rate.

17 citations


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