Exploring topological data analysis for information extraction: application to recognition of Arabic machine-printed numerals

This manuscript explores the capability of topological data analysis (TDA) based on homology theory (HT: a subfield of algebraic topology) to extract relevant information for recognition of confusing Arabic machine-printed numerals. In fact, topological properties may significantly reduce the confusion between some numerals such as “1” and “4” in the context of small data sets. These two latter digits differ in the sense that digit 1 has no hole and digit 4 has one hole. Our contribution consists of evaluating the contribution of TDA with its invariant descriptors such as Betti numbers in machine-printed Arabic numerals recognition. Our investigation is driven by the following set of actions: (i) we extract Betti numbers invariant features of each numeral image and partition the ten numerals into three different clusters with respect to these features. (ii) We then perform a classification by assigning a test image to its corresponding cluster, and map this image to a numeral using dynamic-time warping as a metric defined in the Freemans’ chaincode space. We compared our proposed approach with major state-of-the-art methods depicting various ways of using TDA in character recognition. The advantages and limitations of TDA (including its pros and cons) are discussed further based on numeral recognition results.