Handwritten Digits Recognition Using Particle Swarm Optimization

Ba-Karait, Nasser Omer Sahel; Shamsuddin, Siti Mariyam

doi:10.1109/ams.2008.141

Cited by 14 publications

(4 citation statements)

References 9 publications

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“…Ba-Karait, et al [5] developed recognition base on particle swarm optimization (PSO), to categorize the features into two categories: local and global. Features extracted from the character that has been thinned.…”

Section: Related Researchmentioning

confidence: 99%

Handwriting recognition using a combination of structural elements similarity

Jaelani

Supriana

2014

2014 International Conference on Data and Software Engineering (ICODSE)

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Today, good performance of handwriting recognition system has high complexity or complex computation especially in training and classification. We have developed an offline handwriting recognition system with structural approach. Each character through the stages of pre-processing, structural feature extraction and classification process using a combination of similarity endpoint, branch, line and curve, loop, number and position of each feature obtained from the endpoint and branch. This research focuses on feature extraction stage and classification process. Classification process performed using three stages: selection of dataset, mounting features and calculation similarity. Because of acquisition process of handwriting were performed using offline method, then confounding elements becomes very high. The approach taken in this research can be improved its level accuracy of detection digit number to 89,80%, capital letters 86,60% and normal letter 84,92%.

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Section: Related Researchmentioning

confidence: 99%

Handwriting recognition using a combination of structural elements similarity

Jaelani

Supriana

2014

2014 International Conference on Data and Software Engineering (ICODSE)

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“…The result from these feature combinations can improve an accuracy rate up 97,4%. The research was implemented only on recognition digit number, so for the letter-writing recognition should be inc classification features from each of these feat Ba-Karait, et al [7] developed reco particle swarm optimization (PSO), to categ into two categories: local and global. Featur the character that has been thinned.…”

Section: Related Researchmentioning

confidence: 99%

Handwriting recognition using river-lake skeletonization with combination of structural elements similarity classification

Supriana

Jaelani

2014

2014 International Conference on Electrical Engineering and Computer Science (ICEECS)

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This research try to apply the thinning stage using a river-lake algorithm. It has an advantage in the algorithm speed. The algorithm approach collects points which are assumed as the center point in vertical and horizontal set which every vertical point is connected to horizontal set through projection. The result of the skeleton extraction has single-pixel width which represents starting shape and is conducted in high speed process. In addition, feature extraction and classification using a structural approach, so that the classification process uses a combination of similarity of endpoints, branch points, lines, curves and rings (loops), the number and position of each character features that obtained through solving process of the endpoint, branches and junctions. Stages classification is done in three stages, namely the stage of selecting a dataset, matching features and similarity calculation. The approach taken in this research, can be prove that this method can be apply to recognition technique.

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“…Since, PSO works on local level (particle level) and global level (swarm level), where many solutions are suggested for the problem and the best solution among them is selected. Furthermore, PSO is still not tested for handwriting identification, but it could revealed a high performance in some related fields like pattern classification (Tu et al, 2006;Huang and Kechadi, 2006), signature verification (Das and Dulger, 2007), handwriting digit recognition (Sahel Ba- Karait and Shamsuddin, 2008).…”

Section: Introductionmentioning

confidence: 99%

Swarm-Based Feature Selection for Handwriting Identification

Abdl

Hashim²

2010

Journal of Computer Science

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Problem statement: Handwriting identification is the study for identifying or verifying the writer of a given handwritten document. Since the handwriting features are the cornerstone in the writers' classification process, the classifier accuracy is sensitive in terms of how the writers are scored based on the used features. Approach: In this study, we introduced swarm intelligence as a features weighting mechanism to differentiate between the features having high importance and those having low importance in the identification process. The weights obtained from the swarm experiments were used to adjust the features scores and then to identify the most important subset feature for the writers classification process. Results: The experiments results showed that a significance influence of the feature weights in the handwriting identification process. Conclusion: This communication investigated the influence of the feature importance in the handwriting identification process. Binary Particle Swarm Optimization (BPSO) is used as feature selection method and Euclidian Distance (ED) is used as an evaluation function for the BPSO. The BPSO is trained using 956 words of the off-line IAM data (English handwriting) to learn the feature weights. Each word is represented by 29 statistical features

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Handwritten Digits Recognition Using Particle Swarm Optimization

Cited by 14 publications

References 9 publications

Handwriting recognition using a combination of structural elements similarity

Handwriting recognition using a combination of structural elements similarity

Handwriting recognition using river-lake skeletonization with combination of structural elements similarity classification

Swarm-Based Feature Selection for Handwriting Identification

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