alexa Mistreatment Multiclass in Handwritten Character Recognition SVM Classification with Hybrid Feature Extraction
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
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Research Article

Mistreatment Multiclass in Handwritten Character Recognition SVM Classification with Hybrid Feature Extraction

Dr.Kathir.Viswalingam1, G.Ayyappan2
  1. Dean (R&D), Bharath University, Chennai, India1
  2. Assistant Professor, Department of Information Technology, Bharath University, Chennai, India2
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Abstract

In this paper, we tend to describe hybrid feature extraction for offline written character recognition. The projected technique could be a hybrid of structural, applied math and correlation options. Within the opening, the projected technique identifies the kind and placement of some elementary strokes within the character. The strokes to be hunted for comprise horizontal, vertical, positive slant and negative slant lines–as we tend to observe that the structure of any character are often approximated with the assistance of a mix of straightforward line strokes. The strokes are known by correlating completely different segments of the character with the chosen elementary shapes. These normalized correlation values at completely different segments of the character offer correlation options. For creating feature extraction additional strong, we tend to add within the second step sure structural/statistical options to the correlation options. The additional structural/statistical options are supported projections, profiles, invariant moments, endpoints and junction points. This increased, powerful combination of options leads to a 157-variable feature vector for every character, that we discover adequate enough to unambiguously represent and determine every character. Prior, written character recognition downside has not been self-addressed the means our projected hybrid feature extraction technique deals with it. The extracted feature vector is employed throughout the coaching section for building a support vector machine (SVM) classifier. The trained SVM classifier is after used throughout the testing section for classifying unknown characters. Experiments were performed on written digit characters and uppercase alphabets taken from completely different writers, with none constraint on style. The obtained results were compared with some connected existing approaches. Attributable to the projected technique, the results obtained show higher potency concerning classifier accuracy, memory size and coaching time as compared to those different existing approaches.

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