Classification and Recognition of Bilingual Text Using Graph Edit Distance Based Degree of Similarity

Abstract

Objectives: Graph Edit distance-based classification and recognition method is introduced in this study for bilingual characters. Specifically, this method aims to classify characters first and then recognize them in the 2nd level. Methods: This study combines both exact graph matching and inexact graph matching techniques to achieve better Recognition. The exact graph matching technique classifies characters by considering the number of vertices and edges as features to classify. Inexact graph matching uses an algorithmic model to measure the degree of similarity in the length of edit operations. Findings: The proposed model can compute, if not optimal, sequences all the time, at least near-optimal edit sequence. The proposed model is based on dynamic programming. Edit distance is estimated by comparing preorder sequences of the respective binary trees. Finally, this model uses the loss function to recognize text accurately. To bring out the practicality of the proposed model, we present a case study on the classification of English alphabets and Kannada conjunct consonants. Novelty: (i) A hybrid approach for converting a large class classification problem into a small classification problem that takes advantage of both exact and inexact graph matching techniques. (ii) An algorithmic model is designed to represent each character as a unique string. (iii) Uses dynamic programming in the right places to get the optimal tree, and (iv) to convert the graph matching problem to a string matching problem reduces the time complexity of the problem.

Arbitrary Oriented Scene Text Recognizer (AOTR)

“…The technique used in [13][14][15][16] is OCR for scene text recognition but works with only regular and fixed-length text recognition, and it is not suitable for varied text lengths. In [17], instead of using ready OCR for text recognition, a novel approach is introduced to address multi-oriented and bilingual text issues. It introduces graph representation of characters and dynamic programming to reduce large classification problems into a small classification problem.…”