Fast Extreme Learning Machine for Berber Handwritten Latin Script

Abstract

This article deals with the problem of Berber handwritten character recognition using Extreme Learning Machine. This paradigm has gained significant attention in pattern recognition field thanks to its efficient learning speed and its high accuracy. In this paper, we have used a fast Extreme Learning Machine to recognize efficiently the Latin Berber characters. So, the proposed ELM has been trained over a Berber-MNIST dataset containing images of Amazigh alphabets. This algorithm learns much faster than traditional popular learning algorithms thanks to the use of JAX library which contains several functions to reduce the execution time of our solution. The simulation results show that the handwritten recognition system based on our developed extreme learning machine decreases computational cost and reduces the time required for the whole recognition process. Furthermore, the developed ELM achieves a high performance in terms of recognition accuracy.

The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data

“…A fast-extreme learning machine was used to efficiently recognize Latin Berber characters. The results indicated that the handwriting recognition system based on ELM reduced computational complexity and shortened the time required in the entire recognition process [8]. To accurately obtain the strength of coal gasification slag filling materials, SUO Y et al constructed a GA-ELM prediction model.…”