Handwriting is a natural and one of the popular way of communication and it has always been a challenge to recognize it as it can be written in different ways by the same writer. With the advancement in artificial intelligence, convolutional neural networks can be deployed for handwriting recognition. The technical merits and advancement of artificial intelligence have made it widely accepted for pattern recognition and image processing. Wide range of research studies are evident for handwriting recognition in various languages using Convolutional Neural Network (CNN). But not much research is evident for English language cursive handwriting recognition in offline using IAM dataset. This research work aims to explore various convolutional neural networks which can be deployed for recognition of English language cursive handwritten characters. The methodology consists of collection and preprocessing of data, feature extraction, classification, evaluation, and enhancement of the model. Two models were deployed using GPU for the IAM open dataset. The first model using CNN with varying parameters yielded an accuracy of 92.6% and the second hybrid model which was deployed using CNN and Recurrent Neural Network (RNN), yielded an accuracy of 95.4%. The future direction is to enhance the accuracy of the model using various optimization techniques and increase the number of convolutional layers.