Handwritten Text Image Authentication using Back Propagation

Authentication is the act of confirming the truth of an attribute of a datum or entity. This might involve confirming the identity of a person, tracing the origins of an artefact, ensuring that a product is what it’s packaging and labelling claims to be, or assuring that a computer program is a trusted one. The authentication of information can pose special problems (especially man-in-the-middle attacks), and is often wrapped up with authenticating identity. Literary can involve imitating the style of a famous author. If an original manuscript, typewritten text, or recording is available, then the medium itself (or its packaging - anything from a box to e-mail headers) can help prove or disprove the authenticity of the document. The use of digital images of handwritten historical documents has become more popular in recent years. Volunteers around the world now read thousands of these images as part of their indexing process. Handwritten text images of old documents are sometimes difficult to read or noisy due to the preservation of the document and quality of the image [1]. Handwritten text offers challenges that are rarely encountered in machine-printed text. In addition, most problems faced in reading machine- printed text (e.g., character recognition, word segmentation, letter segmentation, etc.) are more severe, in handwritten text. In this paper we Here in this paper we proposed a method for authenticating hand written text images using back propagation algorithm..

💡 Research Summary

The paper titled “Handwritten Text Image Authentication using Back Propagation” proposes a biometric‑like authentication scheme that treats a handwritten image (or a graphical password) as a password vector for a neural network. The authors begin by outlining the general concept of authentication and the challenges posed by handwritten documents, noting that most optical character recognition (OCR) systems are optimized for printed text and therefore perform poorly on cursive or irregular handwriting. They briefly review offline and online handwriting recognition, feature extraction methods, and the use of neural networks for pattern classification.

The core of the proposed method consists of four processing stages. First, the input image is read pixel‑by‑pixel and the Red, Green, and Blue (RGB) components of each pixel are extracted. Second, each RGB value is normalized to the range