<p>Character recognition is essential in so many aspects of modern life. Although there have been a lot of studies performed on handwritten character recognition, there is less work done on regional languages, particularly in the Tamil language. Analysing Tamil handwritten characters plays a major challenging task owing to the excessive diversity of writing patterns, differing sizes and orientation angles of the characters. A significant barrier still exists in the proper identification of complexly formed compound handwritten characters. Through the learning of discriminating qualities from enormous volumes of raw data, recent developments in neural networks have made significant progress in handwriting recognition. Therefore, this research work aims to develop a novel hybrid recognition model based on improved particle swarm optimization (IPSO) and feed forward back-propagation neural network (FFBNN) for classifying the Tamil handwritten characters. Primarily, the digitized text is pre-processed and segmented with the aid of the modified region growing algorithm. Afterward, the adaptive cuckoo search optimization algorithm can be carried out to extract the different influential features. These extracted features are then fed to the FFBNN model for identifying the Tamil handwritten characters where the IPSO is used to optimize the weights in the FFBNN model. The performance results expose that the proposed framework acquires a superior detection accuracy of 99.01% when compared with other traditional recognition approaches.</p>