A genetic algorithm for on-line cursive handwriting recognition

Ménier, Gildas; Lorette, G; Gentric, P.

doi:10.1109/icpr.1994.577002

Cited by 10 publications

(2 citation statements)

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“…Several temporal features have been used for script recognition in general and for on-line Devanagari script recognition in particular [22,14,33,11]. We discuss feature set based on directional properties of the curve connecting two…”

Section: Direction Based Feature Extractionmentioning

confidence: 99%

A Framework for On-Line Devanagari Handwritten Character Recognition

Kopparapu¹,

L²

2014

Preprint

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The main challenge in on-line handwritten character recognition in Indian language is the large size of the character set, larger similarity between different characters in the script and the huge variation in writing style. In this paper we propose a framework for on-line handwitten script recognition taking cues from speech signal processing literature. The framework is based on identifying strokes, which in turn lead to recognition of handwritten on-line characters rather that the conventional character identification. Though the framework is described for Devanagari script, the framework is general and can be applied to any language.The proposed platform consists of pre-processing, feature extraction, recognition and post processing like the conventional character recognition but applied to strokes. The on-line Devanagari character recognition reduces to one of recognizing one of 69 primitives and recognition of a character is performed by recognizing a sequence of such primitives. We further show the impact of noise removal on on-line raw data which is usually noisy. The use of Fuzzy Directional Features to enhance the accuracy of stroke recognition is also described. The recognition results are compared with commonly used directional features in literature using several classifiers.

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Section: Direction Based Feature Extractionmentioning

confidence: 99%

A Framework for On-Line Devanagari Handwritten Character Recognition

Kopparapu¹,

L²

2014

Preprint

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“…The problem of handwriting recognition can be classified into two main groups, off-line and on-line recognition, according to the format of handwriting inputs (Mener et al, 1994;Kherallah et al, 2008). In offline recognition, only the image of the handwriting is available, while in the on-line case temporal information such as pen tip coordinates, as a function of time, is also available.…”

Section: Introductionmentioning

confidence: 99%

A Tool to Develop Arabic Handwriting Recognition System Using Genetic Approach

Aljuaid¹,

Muhammad²,

Sarfraz³

2010

Journal of Computer Science

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Problem statement: Significant movement has been made in handwriting recognition technology over the last few years. Up until now, Arabic handwriting recognition systems have been limited to small and medium vocabulary applications, since most of them often rely on a database during the recognition process. The facility of dealing with large database, however, opens up many more applications. Approach: This study presented a complete system to recognize off-line Arabic handwriting image and Arabic handwriting and printed text database AHPD-UTM that used to implement and test the system. That system start from preprocessing and segmentation phases that deepened on thinning the image and found the V and H projection profile until recognition phase by genetic algorithm. Results: The genetic algorithm stand on feature extraction algorithm that defined six feature for each segment beak. The system can be recognized Arabic handwriting with 87% accuracy. The confusion and rejection rates are 8.4, those causes for several problems like characters with broken loops and character segmentation problem. Conclusion: Peak connection solved some of the segmentation problems and helped to provide better accuracy

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