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 task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.

使用图像处理的自动银行支票验证是对现有支票截断系统的补充，并且为在最少人工干预下处理银行支票提供了另一种方法。在清算银行支票和货币交易时，这不仅应可靠可靠，而且应节省时间，这是人口众多国家的主要因素之一。为了执行支票验证的任务，我们开发了一种工具，该工具获取支票传单的关键组成部分，这对于使用图像处理和深度学习方法进行支票清算的任务至关重要。这些组成部分包括银行分行代码，支票号码，合法以及礼节性金额，帐号和签名模式。我们的创新旨在通过重新创新需要自动系统干预的其他基于支票的合格货币交易系统来使银行系统受益。在这项研究中，我们使用了银行技术开发与研究学院（IDRBT）的检查数据集和基于深度学习的卷积神经网络（CNN），从而为手写数字字符识别提供了99.14％的准确性。这提高了银行支票手写部分的准确性和精确评估。对于机器打印的脚本，我们使用了MATLAB内置的OCR方法，并且达到了令人满意的精度（97.7％），对于签名的验证，我们还使用了尺度不变特征变换（SIFT）来提取特征，并使用了支持向量机（SVM）作为分类器