The offline handwritten text recognition is critical tasks, which need to be accomplished to move towards a paperless environment. In this paper, a hybrid handwritten text recognition system is proposed using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In the proposed system, IAM dataset is used for training and testing. Totally 87,292 images are used for training and 4,316 images are used for testing. In the proposed system, five distinct features are extracted from the database. In this system, two different classifiers are used for classification namely CNN and RNN. The results obtained are shown in the paper. From the result, RNN performs better than CNN.

Hybrid handwritten text recognition, IAM dataset, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).