Research on recognition algorithm of free handwritten numerals based on combined structural features

Zhao, Dadong; Song, Jeong-Young; Mousavinezhad, S. Hossein

doi:10.1109/eit.2013.6632665

Cited by 3 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al [4] developed free handwriting digit recognition base on combination of structural features such us end points, three and four crossing point, horizontal line, and position of them. The result from these feature combinations can improve an accuracy rate up 97,4%.…”

Section: Related Researchmentioning

confidence: 99%

See 1 more Smart Citation

Handwriting recognition using a combination of structural elements similarity

Jaelani

Supriana

2014

2014 International Conference on Data and Software Engineering (ICODSE)

View full text Add to dashboard Cite

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%.

show abstract

Section: Related Researchmentioning

confidence: 99%

“…At this stage, after pre-processing is done, continued to features extraction. The features extraction process is done based on Zhao's research of structural features[4] modified with the addition of the extracted features. Zhao studies only use the feature point position and the end of the branch, the availability and position of the horizontal line.…”

mentioning

confidence: 99%

Handwriting recognition using a combination of structural elements similarity

Jaelani

Supriana

2014

2014 International Conference on Data and Software Engineering (ICODSE)

View full text Add to dashboard Cite

show abstract

“…Zhao et al [6] developed free handwriting digit recognition base on combination of structural features such us end points, three and four crossing point, horizontal line, and position of them. The result from these feature combinations can improve an accuracy rate up 97,4%.…”

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)

View full text Add to dashboard Cite

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.

show abstract

Scale conjugate gradient based learning applied to handwritten digit classification

Kumari

Rawat

2017

2017 Conference on Information and Communication Technology (CICT)

View full text Add to dashboard Cite

Research on recognition algorithm of free handwritten numerals based on combined structural features

Cited by 3 publications

References 6 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

Scale conjugate gradient based learning applied to handwritten digit classification

Contact Info

Product

Resources

About