Open AccessUnconstrained handwriting recoganization basedon neural network using connectionist temporal classification token passing algorithm
Author(s) -
Pinagadi Venkateswararao,
S. Murugavalli
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.9.9825
Subject(s) - connectionism , computer science , security token , artificial neural network , handwriting , spotting , scripting language , speech recognition , process (computing) , keyword spotting , artificial intelligence , adaptation (eye) , cursive , token passing , natural language processing , programming language , physics , computer security , optics
Recognition of human handwriting which offers the new way to improve the computer interface with the human and this process is very much useful for documents.Keyword spotting refers the spontaneous recognition of handwritten text, letter, and scripts from historical hand written books and the procedure of recovering all instance of a known keyword from an article. With a specific end goal to choose new components this paper, propose "a repetitive neural system manually written acknowledgment framework" for watchword spotting.The watchword seeing is finished utilizing an adjustment of the connectionist temporat classification Token Passing calculation in coincidence with a repetitive neural system. The proposed watchword spotting technique for written by hand message utilizing neural system, with another adaptation of connectionist temporat classification Token Pass calculation with quick and reliable catchphrase spotting can be executed without utilizing any content line or portioning separate words.