Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, digitization and processing of current paper documents describe efficient role in creation a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. neural networks can be trained quickly thanks to lot data other algorithmic advancements. Various methods extracting text from manuscripts developed literature. To extract features written Telugu Text image having some network approaches like convolution (CNN), recurrent (RNN), long short-term memory (LSTM). Different deep related are widely used identification Text; literature documents. For automatic script efficiently eliminate noise semantic present Text, this paper, proposes Novel Heuristic Advanced Neural Network based Categorization Model (NHANNTCM) on sequence-to-sequence feature extraction procedure. Proposed approach extracts using RNN then represents format advanced performs both encoding decoding identify explore visual sequence input data. The classification accuracy rates words, numerals, characters, sentences, corresponding sentences were 99.66%, 93.63%, 91.36%, 99.05%, 97.73% consequently. Experimental evaluation extracted with revealed which textured i.e. TENG shown considerable operations applications such as private information protection, security defense, personal handwriting signature identification.