A review on various Handwritten cursive characters segmentation techniques

C.V, Aravinda; Prakash, H. N.

doi:10.1109/icatcct.2016.7912080

Cited by 6 publications

(1 citation statement)

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“…However, preprocessing of images if not done properly can lead to inaccurate results. This research [14] proposes a handwritten signature verification and recognition using Artificial Neural Network and contour-based features. The dataset included 5600 genuine and 5600 forged offline signatures.…”

Section: B Deep Learning Based Algorithms 1) Convolutional Neural Net...mentioning

confidence: 99%

Forgery Detection on Handwritten Signatures using Convolutional Neural Networks

Pradhan

2023

IJRASET

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Handwritten signatures play a significant part in numerous parts of our day-to-day events and aid in authenticating our personal information all over the world from banks to many government and private institutions. On the other hand, the usage of these handwritten signatures is accompanied by the problems of signature replication, counterfeit signatures and identity theft by both professional and amateur people alike. And so, there is a need for a system to help in differentiating and isolating the real signatures from their copied lookalikes but this task turns out to be really challenging. In recent years, there have been lots of advances in the field of Deep Learning, which is increasingly becoming a part of daily lives. One such deep learning technique called Convolutional Neural Networks, is an interesting tool for analysing pictures and other types of data. This method could be applied to detect and analyse handwritten signatures taken as inputs in order to classify if it is original or forged.

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Section: B Deep Learning Based Algorithms 1) Convolutional Neural Net...mentioning

confidence: 99%