Paper Title
Handwritten Word Recognition using Different Architectures of Convolutional Neural Network

Abstract
A language is mainly a combination of words. To recognize a particular means to recognize the words. Word recognition is very important for every language. The characters that are included in a character's image are identified by handwriting recognition software [1]. Handwritten word recognition (HWR) is not done accurately still. In previous times, the used technique was less accurate. To solve the problem, we proposed a technique in the proposed method. Five architectures of the Convolutional neural network (CNN) have been used in the proposed method. Those are ResNet-50, Alex Net, VGG-16, Efficient B2, and Efficient B3. Moreover, to train all models we use 2 datasets. First is Zilla (64), and Test (10). In the Zilla (64) dataset, there are 64 classes and almost 5000 input images. The 64 classes define 64 districts of Bangladesh. For Zilla (64) dataset, we get 92.12% validation accuracy from ResNet-50, 85.77% validation accuracy from Alex Net, 97. 69% validation accuracy from Efficient B2, 92.22% validation accuracy from Efficient B3, 77.31% validation accuracy from VGG-16. Almost every model gives a good accuracy rate compared to the existing work. A little bit of error was found because of the resemblance and lofty curvature nature of the data.