Automatic handwritten character recognition of Devanagari language: a hybrid training algorithm for neural network

• Published in: Evolutionary intelligence Vol. 15; no. 2; pp. 1499 - 1516
• Main Authors: Yawalkar, Prashant Madhukar, Kharat, Madan Uttamrao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022; Springer Nature B.V
• Subjects: Algorithms; Applications of Mathematics; Artificial Intelligence; Bioinformatics; Character recognition; Classification; Cluster analysis; Clustering; Control; Delaunay triangulation; Documents; Engineering; Feature extraction; Handwriting recognition; Image processing; Image segmentation; Machine learning; Mathematical and Computational Engineering; Mechatronics; Neural networks; Object recognition; Optimization; Robotics; Special Issue; Statistical Physics and Dynamical Systems; Tactics; Vector quantization; Neural network classification; Devanagari language; Feature extraction; Character recognition; Pre-processing; Optimization
• ISSN: 1864-5909; 1864-5917
• DOI: 10.1007/s12065-021-00597-8

In the field of image processing and Artificial Intelligence the character recognition and handwritten recognition have been emerging as one of the active and challenging research areas. In recent years, there has been a notable development in the research associated with character recognition in handwritten Devanagari documents. To improve the recognition performance, this paper tactics to develop new handwritten character recognition model using an improved machine learning approach. The proposed character recognition model includes four stages like pre-processing, Segmentation, Feature Extraction, and Classification. Initially, the scanned handwritten document for Devanagari language is subjected to pre-processing, which includes stages like, RGB to gray, thresholding, complement of image, morphological operations, linearization and noise removal using Median Filter. Then, the characters of the pre-processed image are segmented using k-means clustering that is a popular method for cluster analysis. Further, the features like, Kirsch Directional Edge, Freeman chain code and neighborhood distance weight using Delaunay Triangulation are extracted from the segmented characters. Subsequently, the classification of characters is done using Neural Network (NN) where the new training algorithm is used, in which the weights are optimized using a hybrid optimization by combining Lion Optimization Algorithm and Grey Wolf Optimization (GWO). Here, the update procedure of GWO is based on LA algorithm and hence, the proposed algorithm is named as Lion Updated GWO (LU-GWO). To the next of the implementation, a valuable comparative analysis confirms the improved performance of the proposed LU-GWO-NN model over conventional methods in classifying the consonants, numerals, and vowels from different characters.