This paper deals with the fundamental problem of automatic assessment of appearance of seam puckers on suits, and suggests possibilities for practical usage. Presently, evaluations are done by inspectors who compare standard photographs of suits to test samples. In order to avoid human errors, however, a method of automatic evaluation is desired. We process the problem as pattern recognition. As a feature we use fractal dimensions. The fractal dimensions obtained from standard photographs are used as template patterns. To make it easier to calculate fractal dimensions, we plot a curve representing the appearance of seam puckers, from which fractal dimensions of the curve can be calculated. The seam puckers in gray-scale images are confused with the material's texture, so the seam puckers must be enhanced for a precise evaluation. By using the concept of variance, we select images with seam puckers and enhance only the images with seam puckers. This is the novel aspect of this work. Twenty suits are used for the evaluation experiment and we obtain a result almost the same to the evaluation gained by inspection. That is, the evaluation of 11 samples is the same as that gained by inspection, the results of 8 samples differ by 1 grade, and the evaluation of 1 sample has a 2-grade difference. The results are also compared to the evaluation of the system using the Daubechies wavelet feature. The result of comparison shows that the present method gives a better evaluation than the system using the Daubechies wavelet.