• J N SUDARSHAN
• KALPAJ PISE
• MANIKANTA YA

Despite swift advancements happening in digital technology, financial institutions like banks still rely upon conventional mediums of processing bank cheques by humans. The process is cumbersome and takes a couple of days to transfer money, which involves verification by the intermediaries. This leads to high time and costs. In this paper, we propose an automated system that extracts relevant details of a bank cheque like Payee Name, Amount, Date, Bank Name using Optical Character Recognition and Deep Learning and verifies the signature on the cheque with the existing signature stored in the database using feature extraction and principal component analysis. The signature for a new user is stored using its hash value for security purposes. The proposed system uses a modified convolution neural network to extract the handwritten content on a cheque leaf wherein IAM dataset is used to train the model and get the optimized results. This system will facilitate the process and lead to a reduction in time and costs. The efficiency and performance are measured on the self-generated data set of bank cheques.

"Automated Cheque Processing System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.b712-b717, July-2021, Available :http://www.jetir.org/papers/JETIR2107218.pdf

"Automated Cheque Processing System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppb712-b717, July-2021, Available at : http://www.jetir.org/papers/JETIR2107218.pdf