A New Approach for Turning Handwritten Text into Digitalized Text Using DNN in Real Complex Scenarios

Authors

  • Dr. R. Josphineleela R., Yogashree G. S., Monisha G. S.

Keywords:

Hand written, text, public sectors, Deep neural network, accuracy, stroke edges, performance.

Abstract

Human handwritten character recognition plays a significant role in many public and private sectors, including banking, medicine, and education. Many studies have been conducted on hand-written text recognition to ensure that stroke edges of each character can be accurately recognized. Even though a number of technologies are available to recognize characters, none provide effective recognition of stroke edges. To overcome this problem, Deep Neural Network (DNN) is used as a methodology to identify the edges of the character, so as to convert the handwritten character into a human-readable form. The major challenge is to recognize the edges of each character with a high degree of accuracy. The recognition process is extremely accurate and efficient in identifying the stroke of the handwritten character. According to the overall performance of the proposed approach, it proves to be most efficient in giving a benchmark as well as flexible in conversion with much less time constraint.

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Published

2022-08-02