A Hand Written Digit Recognition Using Convolutional Neural Networks Compared With Decision Tree With Improved Accuracy.

Aim: The aim is to improve and develop a written digit identification to detect practically important issues in pattern recognition using Novel Convolution Neural Networks compared with Decision tree. Materials and Methods: Handwritten digit recognition is performed using Novel Convolution neural networks algorithm Sample size is 66 over decision tree algorithm Sample size is (N=66) with the split size using Table 1 and training using G power 80% and testing dataset 70% and 30% respectively. Results: The retrieval accuracy of the Novel Convolution Neural Networks classifier is (96.42%) and Decision tree is (72.35%), There exists a statistically insignificant difference between the two groups (p=0.193; p>0.05)`. Conclusion: The work has confirmed that the efficiency of the Novel convolution neural network algorithm has given more accuracy value in written digit identification when compared to decision tree algorithm using artificial intelligence algorithm. [ABSTRACT FROM AUTHOR]