Distributed Fast Fourier Transform (DFFT) on MapReduce Model for Arabic Handwriting Feature Extraction Technique via Cloud Computing Technologies

摘要

The choice of relevant features is very important and decisive step when building a handwriting recognition system. Indeed, a good choice can lead to a powerful system and vice-versa. Fast Fourier Transform (FFT) is amongst adequate feature extraction technique to achieve such an objective. Typical Arabic handwriting recognition tasks based on FFT and especially when dealing with a big and massive amount of Arabic handwriting documents require enough processing power that could not be provided by current state-of-the-art workstations. Distributed computing architectures and infrastructures appear to be a solution to afford such a mission. Our aim is indeed to distribute the FFT feature extraction techniques using the MapReduce programming model for Arabic handwriting feature extraction using Cloud Computing architecture. Experiments were conducted on the MapReduce model via the Amazon Web service (AWS) Cloud Computing architecture, with a real large scaled dataset from the IFN/ENIT database. Performance analysis revealed the viability of our investigation; moreover, it confirms also that such infrastructures can speed up substantially the entire pattern recognition system.