Automated bank cheque verification using image processing and deep learning methods

Agrawal, Prateek; Chaudhary, Deepak; Madaan, Vishu; Zabrovskiy, Anatoliy; Prodan, Radu; Kimovski, Dragi; Timmerer, Christian

doi:10.1007/s11042-020-09818-1

Cited by 39 publications

(18 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI has become state of the art for image analysis [ 25 ] and plays a role in supporting clinical decision making with respect to diagnosis and risk stratification [ 7 , 26 ]. Lakhani et al [ 27 ] recently reported a DL with CNN system to rapidly and accurately classify TB on chest radiographs with a sensitivity of 97.3% and specificity of 100.0%.…”

Section: Discussionmentioning

confidence: 99%

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

et al. 2021

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

et al. 2021

View full text Add to dashboard Cite

“…The pre-processing methods like noise removal and image normalization processes are utilized on the input image, earlier serving to the pixel classification process. Agrawal et al [11] used the scale-invariant feature transform method for feature extraction. Madaan et al [12] implemented convolutional neural networks for medical image classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images

Reshma¹,

Al-Atroshi²,

Nassa³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts (hair, gel bubble, ruler marker), blurry boundary, poor contrast, and variable sizes and shapes of the lesion images. This study develops intelligent multilevel thresholding with deep learning (IMLT-DL) based skin lesion segmentation and classification model using dermoscopic images to address these problems. Primarily, the presented IMLT-DL model incorporates the Top hat filtering and inpainting technique for the pre-processing of the dermoscopic images. In addition, the Mayfly Optimization (MFO) with multilevel Kapur's thresholding-based segmentation process is involved in determining the infected regions. Besides, an Inception v3 based feature extractor is applied to derive a valuable set of feature vectors. Finally, the classification process is carried out using a gradient boosting tree (GBT) model. The presented model's performance takes place against the International Skin Imaging Collaboration (ISIC) dataset, and the experimental outcomes are inspected in different evaluation measures. The resultant experimental values ensure that the proposed IMLT-DL model outperforms the existing methods by achieving higher accuracy of 0.992.

show abstract

“…In summary, regression-based techniques like as YOLO and MobileNet SSD are becoming more popular for object recognition. Some of the existing research work related to object detection and identification have been discussed here -Prateek Agrawal et al [7] has suggested a technique for verifying bank checks The following information is used to verify the bank check: cheque number, bank account number, bank branch code, legal and courtesy amount, and signature. They used the IDRBT cheque dataset and deep learning-based CNN to recognise handwritten digits with a high accuracy of 99.14 percent.…”

Section: Literature Surveymentioning

confidence: 99%

Survey On Real Time Multiple Object Detection Using MobileNet-SSD with Opencv

Journal¹

2022

IJSREM

View full text Add to dashboard Cite

The field of computer vision known as real-time object detection is vast, dynamic, and difficult. Real-time object recognition and tracking are critical and difficult problems in many computer vision applications, including video surveillance, robot navigation, and vehicle navigation. Detecting an item in a video sequence is what object detection is all about. Every tracking technique necessitates an object detection mechanism, either in each frame or whenever an item appears for the first time in the video sequence. The practise of identifying one object or numerous objects using a static or dynamic camera is known as object tracking. The availability of powerful computers, high-quality, low-cost video cameras will enhance the need for automated video analysis. Image Localization is used when there is only one object to distinguish in an image, and Object Detection is used when there are multiple objects in an image. The most often used strategies for contemporary deep learning models to perform various tasks on embedded devices are mobile networks and binary neural networks. In this research, we propose a method for distinguishing an item based on the MobileNet deep learning pre- prepared model for Single Shot Multi-Box Detector (SSD). This technique is used to recognise objects in a video stream in real time and for webcam broadcasting. Following that, we use an object detection module to determine what is in the video stream. To complete the module, we combine the MobileNet and SSD frameworks to create a fast and efficient deep learning-based item detection technique.

show abstract

Automated bank cheque verification using image processing and deep learning methods

Cited by 39 publications

References 72 publications

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images

Survey On Real Time Multiple Object Detection Using MobileNet-SSD with Opencv

Contact Info

Product

Resources

About