Convolutional Neural Network Approach for Extraction and Recognition of Digits from Bank Cheque Images

Holi, Ganga; Jain, Divya

doi:10.1007/978-981-13-5802-9_31

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Histograms encompass a collection of heuristic ideas used to extract the courtesy amount region. Extraction techniques based on enclosing boxes and linked component analysis have also been emphasised in the literature [5]. Beginning with grayscale conversion of the cheque image to reduce file size, subsequent vertical and horizontal scanning of the grayscale image to locate the AROI, and finally segmentation of the AROI regions, the proposed technique in [6] uses Cartesian coordinate space to divide the cheque image into interesting regions.…”

Section: IImentioning

confidence: 99%

Data Processing from Bank Cheques by Utilising Split Attribute Character Analysis and Multi Branch Network Forest Classifier Techniques

Hitesh Chaitanyaswami

2024

IJRITCC

View full text Add to dashboard Cite

Bank cheques are primarily used for conducting financial transactions, resulting in their substantial daily processing volumes worldwide. The automation of the whole process of recognising and verifying cheques has the potential to significantly reduce both the time and expenditures associated with cheque execution. The field of automatic bank cheque processing system is now gaining prominence in the realm of computer vision, image processing, pattern recognition, machine learning, and deep learning. The study places particular emphasis on the sequential processes involved in the automated bank Cheque processing system, including picture capture, pre-processing, and extraction and identification. This article provides an overview of the sequential processes included in the automated data extraction system. This research aims to propose strategies for the automated processing of bank cheque images via the use of Split Attribute character analysis and Multi branch network forest classifier. The study indicates that the recommended technique demonstrates satisfactory performance by achieving high levels of accuracy, precision, recall, and F score.

show abstract

Section: IImentioning

confidence: 99%

Data Processing from Bank Cheques by Utilising Split Attribute Character Analysis and Multi Branch Network Forest Classifier Techniques

Hitesh Chaitanyaswami

2024

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Jha et al [52] used CNN to extract handwritten characters on checks. Holi et al [53] proposed using CNN for identifying handwritten digits in bank checks. Wadhwa et al [54] proposed using geometrical and structural features, like stroke width and direction, with the radial-basis SVM and random forest to verify the style of writing on bank checks.…”

Section: Autonomous Check Processing and Verification Using Add-kycmentioning

confidence: 99%

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

2023

View full text Add to dashboard Cite

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer's KYC and anonymise the collected visual data to ensure the customer's privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of inperson banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.

show abstract

“…Automatic check processing involves several important modules, such as courtesy amount recognition (e.g., [18][19][20]), literal amount recognition (e.g., [21][22][23][24][25]), date verification (e.g., [26,27]), and signature verification (e.g., [13,28]) in addition to tasks such as recognizing check numbers and account numbers. Interested readers can refer to [29] for an old but relevant survey on Arabic check processing and to [14] for automatic check processing, in general.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition

Ahmad

2023

Sensors

View full text Add to dashboard Cite

Courtesy amount recognition from bank checks is an important application of pattern recognition. Although much progress has been made on isolated digit recognition for Indian digits, there is no work reported in the literature on courtesy amount recognition for Arabic checks using Indian digits. Arabic check courtesy amount recognition comes with its own unique challenges that are not seen in isolated digit recognition tasks and, accordingly, need specific approaches to deal with them. This paper presents an end-to-end system for courtesy amount recognition starting from check images as input to recognizing amounts as a sequence of digits. The system is a hybrid system, combining rule-based modules as well as machine learning modules. For the amount recognition system, both segmentation-based and segmentation-free approaches were investigated and compared. We evaluated our system on the CENPARMI dataset of real bank checks in Arabic. We achieve 67.4% accuracy at the amount level and 87.15% accuracy at the digit level on the test set consisting of 626 check images. The results are presented with detailed analysis, and some possible future work is identified. This work can be used as a baseline to benchmark future research in Arabic check courtesy amount recognition.

show abstract

Convolutional Neural Network Approach for Extraction and Recognition of Digits from Bank Cheque Images

Cited by 3 publications

References 11 publications

Data Processing from Bank Cheques by Utilising Split Attribute Character Analysis and Multi Branch Network Forest Classifier Techniques

Data Processing from Bank Cheques by Utilising Split Attribute Character Analysis and Multi Branch Network Forest Classifier Techniques

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition

Contact Info

Product

Resources

About