Segmented character recognition using curvature-based global image feature

Belaynesh Chekol,Tuğrul Taşci,Numan Çelebi̇

doi:10.3906/elk-1806-195

Belaynesh Chekol, Tuğrul Taşci + Show 1 more

Open Access

https://doi.org/10.3906/elk-1806-195

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Abstract

Segmented character recognition using curvature-based global image feature

Highlights

The availability of high quality and portable imaging devices today simplifies visual representation of a particular environment through digital images and videos which in turn signify physical and material contents that form a specific scene
Feature set construction and representation The description of the keypoints and their interrelationship is used to form a global feature descriptor represented with a 573-dimensional vector, which is passed to the classification learner subsequently
The results clearly show that curvature-based global features resulted in a higher classification accuracy than scale invariant feature transform (SIFT) (Table 2)

Summary

Introduction

The availability of high quality and portable imaging devices today simplifies visual representation of a particular environment through digital images and videos which in turn signify physical and material contents that form a specific scene. CHEKOL et al./Turk J Elec Eng & Comp Sci and intelligent approaches over time, ensuring fast and accurate character recognition These approaches focus on the selection of appropriate image features and description. Region and contour-based shape features are used to describe image content. Current trends in segmented character and natural scene image text recognition focus on manipulating either local image features or region-based global, shape features. The literature review discerns that the power of contour-based shape features is overlooked and only a handful of related studies are found . A global curvature-based shape feature is generated and applied for segmented character recognition to demonstrate the description power of contourbased global features. The extraction of the global feature relies mainly on the calculation of curvature information at the detected boundaries of the segmented character image.

Segmented character recognition

Proposed method

Image gradient direction calculation

Angle calculation

Conclusions