Translation, Rotation, and Scale Invariant Character Recognition using Modified Ring Projection

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II. INTRODUCTION

Optical Character Recognition (OCR) has been a topic of interest and research for well over a half-century, but Invariant Character Recognition (ICR), which is the recognition of characters written in different positions, orientations, and scales is still a challengeable problem. So far, many research groups have proposed different ICR techniques in the literatures. These methods can be generally divided into five groups: ‎[1],‎[2] Optical techniques, ‎[3],‎[4],‎[5] boundary-based analysis especially via Fourier descriptors, ‎[6],‎[7],‎[8],‎[9] neural network models, ‎[10],‎[11],‎[12],‎[13],‎[14],‎[15],‎[16] invariant moments, ‎[17],‎[18],‎[19],‎[20] and finally, genetic algorithms. ‎[21],‎[22],‎[23],‎[24]

Methods based on the optical techniques such as Scale, Translation, and In-plane Rotation (STIR) invariant transformations usually map an image (intensity function) into a one-dimensional (or even, multi-dimensional) frequency spectrum function. ‎[3] Computer simulations have shown that optical techniques perform well, however, the major disadvantage of them is that they require heavy computational load. In boundary-based analysis methods, the boundary line of a two-dimensional object (character) is represented using a one-dimensional function, i.e. the Shape Signature. One of the common ways to obtain a shape signature is to combine the coordinates of the boundary points to the complex numbers. ( ) Since such a shape signature is a periodic signal, it can be projected to the frequency domain using the Fourier transform, the process that is called boundary-based description via Fourier transformation. ‎[8] Although this technique has a low computation cost, but it cannot deal with disjoint shapes where singl...

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