Offline Natural Scene Character Recognition Using VGG16 Neural Networks

Recognizing printed characters is a little easier than when compared to handwritten characters by offline OCR machines. When the images of the characters are taken from the natural scene, they become hazy and in various angles. Then they are harder to recognize, even though they are printed. In this study, an attempt has been made to identify characters that are colored and in different orientations, using a deep convolutional neural network known as VGG16. The effect on classification accuracy with respect to epochs on the datasets is projected by conducting some experiments. Two Latin character datasets have been considered, such as Chars74kImg and Binary alphanumeric. The Chars74kImg dataset consists of RGB isolated character images taken from the real world, while the Binary alphanumeric dataset comprises binary versions of distinct characters in different orientations. With the Chars74kImg dataset, the suggested model obtains the highest accuracy of 78.04%, far better than the previous techniques found in the literature. Finally, along with quantitative analysis, a comparison of the suggested method to a few preexisting systems is presented.