Classical Handwriting recognition systems depend on manual feature extraction with a lot of previous domain knowledge. It’s difficult to train an optical character recognition system based on these requirements. Deep learning approaches are at the centre of handwriting recognition research, which has yielded breakthrough results in recent years. However, the rapid growth in the amount of handwritten data combined with the availability of enormous processing power necessitates an increase in recognition accuracy and warrants further investigation. Convolutional Neural Networks (CNNs) are extremely good at perceiving the structure of handwritten characters in ways that allow for the automatic extraction of distinct features, making CNN the best method for solving handwriting recognition problems. In this research work, a novel CNN has built to modify the network structure with Orthogonal Learning Chaotic Grey Wolf Optimization (CNN-OLCGWO). This modification is adopted for evolutionarily optimizing the number of hyper-parameters. This proposed optimizer predicts the optimal values from the fitness computation and shows better efficiency when compared to various other conventional approaches. The ultimate target of this work is to endeavour a suitable path towards digitalization by offering superior accuracy and better computation. Here, MATLAB 2018b has been used as the simulation environment to measure metrics like accuracy, recall, precision, and F-measure. The proposed CNN- OLCGWO offers a superior trade-off in contrary to prevailing approaches.
A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model.
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