A Contour-Based Thinning Algorithm with Post-Processing Using Auto-Encoder

One of the critical pre-processing phases in a pattern recognition system is thinning. The process of finding a one-pixel-wide representation of a binary object while preserving its original shape is known as 'thinning'. Thinning makes recognition easier and more efficient due to the fact that the thin image of the object is less complex than the original one. It is also easy to find topological features like endpoints, junction points, lines, curves, etc. The general problem with the thinning method is that it often results in unwanted edges or hairs that deform the original shape of the object and ultimately decrease the system's accuracy. The current proposal presents a contour-based thinning algorithm to address this issue. The fundamental part of the suggested algorithm is to extract a skeleton from the initial binary object. The potential distribution of pixels in the junction region is estimated in the second phase using an auto-encoder trained on similar thinned images in order to provide more accurate thinned images. The third stage employs post-processing to produce an image that is one pixel wide. The proposed method was tested on a handwritten Gujarati numeral database with 99.89% accuracy.