A fuzzy approach for segmentation of touching characters

TL;DR

This paper introduces a novel fuzzy logic-based method for segmenting touching characters, combining multiple features to improve accuracy in Latin printed and handwritten text recognition.

Contribution

It presents a new fuzzy inference system that integrates three features for segmentation, optimized specifically for Latin characters, demonstrating adaptability and effectiveness.

Findings

Achieves good accuracy in challenging touching character cases

Applicable to various datasets with simple tuning

Outperforms some existing segmentation approaches

Abstract

The problem of correctly segmenting touching characters is an hard task to solve and it is of major relevance in pattern recognition. In the recent years, many methods and algorithms have been proposed; still, a definitive solution is far from being found. In this paper, we propose a novel method based on fuzzy logic. The proposed method combines in a novel way three features for segmenting touching characters that have been already proposed in other studies but have been exploited only singularly so far. The proposed strategy is based on a 3--input/1--output fuzzy inference system with fuzzy rules specifically optimized for segmenting touching characters in the case of Latin printed and handwritten characters. The system performances are illustrated and supported by numerical examples showing that our approach can achieve a reasonable good overall accuracy in segmenting characters even on tricky conditions of touching characters. Moreover, numerical results suggest that the method can be applied to many different datasets of characters by means of a convenient tuning of the fuzzy sets and rules.