Comparison the training methods of neural network for English and Thai character recognition

Saenthon, Anakkapon; Sukkhadamrongrak, Natchanon

doi:10.1109/apsipa.2014.7041795

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2020

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“…They were able to achieve approximately 97% of recognition rate. For English language also, due to its widespread usage and commercial usability, Zhu and Wang (2002) [21], Dhande and Kharat (2017a) [22], Pal et al (2006) [23], Dhande and Kharat (2017b) [24], and Saenthon and Sukkhadamrongrak (2014) [25] proposed few approaches for character recognition. Font recognition was done by using a wide array of techniques and they were able to achieve a satisfactory accuracy on text blocks.…”

Section: Related Workmentioning

confidence: 99%

Automated bank cheque verification using image processing and deep learning methods

Agrawal

Chaudhary

Madaan

et al. 2020

Multimed Tools Appl

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Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the

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