SR. NO TOPICS PAGE NO 1) Abstract 6 2) Project Objective 7 3) Introduction 8 3.1) American sign language 9 4) Background 10 4.1) Object Recognition 11 4.12) Large Object Recognition 12 4.13) Shape Recognition 12 5) Digital image processing 13 6) Neural network 14 6.1) Neuron model 15 6.11) Simple Neuron 16 6.12) Transfer function 17 6.13) Supervised learning 17 6.14) Unsupervised learning 18 6.2) Advantages 19 6.3) Limitation 19 6.4) Single perceptron 21 6.41) Perceptron convergence algo. 22 6.42) Linearly separable only 23 7) Matlab 25 8) Approach 26 8.1) Image database 26 8.2) Perceptron implementation 29 8.31) Learning rules 29 .8.32) Linear classification 9) Modules 32
SR. NO TOPICS PAGE NO 1) Abstract 6 2) Project Objective 7 3) 3.1) Introduction American sign language 8 9 4) 4.1) 4.12) 4.13) Background Object Recognition Large Object Recognition Shape Recognition 10 11 12 12 5) Digital image processing 13 6) 6.1) 6.11) 6.12) 6.13) 6.14) 6.2) 6.3) 6.4) 6.41) 6.42) Neural network Neuron model Simple Neuron Transfer function Supervised learning Unsupervised learning Advantages Limitation Single perceptron Perceptron convergence algo. Linearly separable only 14 15 16 17 17 18 19 19 21 22 23 7) Matlab 25 8) 8.1) 8.2) 8.31) .8.32) Approach Image database Perceptron implementation Learning rules Linear classification 26 26 29 29 9) Modules 32