A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Table 1

Some of the most used features in OCR applications.

Type of features	Some of the most used features
Structural	(i) Simple, double, and complex loops; (ii) Loops positions, their types, and their relative locations; (iii) Hills and valleys; (iv) Open curves in different directions; (v) Ascenders and descenders; (vi) Numbers and locations of dots in each character; (vii) Location of dots relevant to baselines; (viii) Starting, ending, branching, crossing, turning, and corner points in image skeleton; (ix) Curvature and length of the image's segments; (x) Length of a character segment relative to other segments; (xi) Location of a character segment relative to center of mass image's skeleton
Statistical	(i) Normalized central, Zernike, pseudo Zernike, fast Zernike, Legendre, orthogonal Fourier-Mellin, rotational, and complex moments extracted from the whole body, only from the main body, or only from the secondary parts of an image; (ii) Fourier and gradient descriptors; (iii) Image area and image perimeter; (iv) Pixels distribution in left, right, up, and down halves of the image; (v) Image density; (vi) Aspect Ratio; (vii) Mean, mode, variance, and 2D standard deviation; (viii) Average and variance of 𝑋 and 𝑌 changes in portions of the image skeleton; (ix) Local maxima points in horizontal and vertical projection histograms; (x) Ratio of horizontal variance histogram to vertical variance histogram; (xi) Ratio of upper half variance to lower half variance of an image; (xii) Thinness ratio; (xiii) The ratio of pixel distribution between two or more parts of the image; (xiv) Center of mass (COM) (center of gravity); (xv) Centroid distance; (xvi) Radial coding; (xvii) Top, bottom, left, and right profile histograms; (xviii) Number of horizontal (row) and vertical (column) transitions; (xix) Number of modified horizontal and vertical transitions; (xx) Outer and inner contour directional chain code histograms; (xxi) Normalized contour chain code descriptors; (xxii) Modified contour chain code descriptors; (xxiii) Fractal, shadow code descriptors; (xxiv) Energy of original image; (xxv) Number of specific points such as end, branch, and cross points; (xxvi) Number of connected components; (xxvii) Relative location of start and end points of an image skeleton; (xxviii) Pen width and line height; (xxix) Baselines positions; (xxx) Histogram of slopes along contour; (xxxi) Skeleton-based N-degree directional descriptors
Transformation	(i) M-band packet wavelet coefficients; (ii) Fourier, DCT, and radon coefficients