End-To-End Deep Learning Based Tamil Handwritten Document Recognition and Classification Model

Handwritten recognition (HR) remains a challenging process in various real-world applications. Tamil handwritten text recognition involves the recognition of text in scanned images. Recognition of handwritten Tamil characters is a tedious process because of the differences in sizes, style and orientation angle. Prior studies concentrated on character-level segmentation and each character was subsequently classified. Segmentation is then used, first at the word level and subsequently at the line level. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized for Tamil HCR. With this motivation, this paper presents an end-to-end deep learning-enabled Tamil handwritten document recognition (ETEDL-THDR) model. The ETEDL-THDR paragraph text recognition can be accomplished by the use of two modules such as line segmentation and line recognition. Initially, the ETEDL-THDR model enables the improvement of the quality of the input images by the use of the median filtering (MF) technique. To create meaningful regions, further line and character segmentation activities are performed. Additionally, a deep convolutional neural network-based MobileNet approach was applied to derive feature vectors. At last, the water strider optimization (WSO) algorithm with a bidirectional gated recurrent unit (BiGRU) model is applied to recognize Tamil characters. An extensive experimental analysis of the ETEDL-THDR model is carried out and the results showed that the ETEDL-THDR model performed better than more recent methodologies with maximum accuracy of 98.48%, precision of 98.38%, a sensitivity of 97.98%, specificity of 98.27%, and F-measure of 98.35%.