Image pattern recognition in identification of financial bills risk management

Li, Huahua; Huang, Chuanchao; Gu, Lihan

doi:10.1007/s00521-020-05261-3

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…Other businesses and tasks built on the above interactions include automated identification of interactions and communications and response generation for autonomous, open-domain, context-aware, multi-turn, multi-level, multilingual and knowledge-based dialogue, interactions and communications. Examples are [4,17,36,38]:…”

Section: The Smart Fintech Ecosystemmentioning

confidence: 99%

Data science and AI in FinTech: an overview

Cao

Yang

2021

Int J Data Sci Anal

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Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and artificial intelligence (DSAI) techniques. Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems. The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, Trade-Tech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart FinTech futures to the DSAI communities.

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Section: The Smart Fintech Ecosystemmentioning

confidence: 99%

Data science and AI in FinTech: an overview

Cao

Yang

2021

Int J Data Sci Anal

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“…People need more comprehensive image feature recognition technology to explore more comprehensive image information and obtain various information contained in images more comprehensively and accurately [ 13 ]. As a relatively mature machine learning technology, deep learning technology can comprehensively extract and recognize image features through deep learning and computational analysis and has become the main technology in image recognition [ 14 ]. The specific principles of image recognition technology under different technologies are shown in Figure 1 .…”

Section: Research Theories and Methodsmentioning

confidence: 99%

Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning

Tan

2022

Computational Intelligence and Neuroscience

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This work aims to improve the feature recognition efficiency of painting images, optimize the style transfer effect of painting images, and save the cost of computer work. First, the theoretical knowledge of painting image recognition and painting style transfer is discussed. Then, lightweight deep learning techniques and their application principles are introduced. Finally, faster convolutional neural network (Faster-CNN) image feature recognition and style transfer models are designed based on a lightweight deep learning model. The model performance is comprehensively evaluated. The research results show that the designed Faster-CNN model has the highest average recognition efficiency of about 28 ms and the lowest of 17.5 ms in terms of feature recognition of painting images. The accuracy of the Faster-CNN model for image feature recognition is about 97% at the highest and 95% at the lowest. Finally, the designed Faster-CNN model can perform style recognition transfer on a variety of painting images. In terms of style recognition transfer efficiency, the highest recognition transfer rate of the designed Faster-CNN model is about 79%, and the lowest is about 77%. This work not only provides an important technical reference for feature recognition and style transfer of painting images but also contributes to the development of lightweight deep learning techniques.

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“…Deep learning is a multi-level representation learning method in which simple but nonlinear modules are combined to change a representation at one level (beginning from the original input) into a higher-level abstract representation 22 . Deep learning-based character recognition is a fundamental and challenging problem in computer vision, and it is widely utilized in business offices 23 , 24 , finance 25 , 26 , medicine 27 , 28 , automotive 29 , 30 , and industrial 31 , 32 . The rich knowledge of context 33 and structured prediction algorithms 34 are combined in these systems to make significant improvements.…”

Section: Related Workmentioning

confidence: 99%

Multiple attention-based encoder–decoder networks for gas meter character recognition

Wang

Ullah

et al. 2022

Sci Rep

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Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image quality of meter dials in natural environments, resulting in poor dial image quality. The remote meter reading system has trouble recognizing dial pictures in extreme settings, challenging it to meet industrial production demands. This paper provides multiple attention and encoder–decoder-based gas meter recognition networks (MAEDR) for this problem. First, from the acquired dial photos, the dial images with extreme conditions such as overexposure, artifacts, blurring, incomplete display of characters, and occlusion are chosen to generate the gas meter dataset. Then, a new character recognition network is proposed utilizing multiple attention and an encoder–decoder structure. Convolutional neural networks (CNN) extract visual features from dial images, encode visual features employing multi-head self-attention and position information, and facilitate feature alignment using the connectionist temporal classification (CTC) method. A novel two-step attention decoder is presented to improve the accuracy of recognition results. convolutional block attention module (CBAM) reweights the visual features from the CNN and the semantic features computed by the encoder to improve model performance; long short-term memory attention (LSTM attention) focuses on the relationship between feature sequences. According to experimental data, our system can effectively and efficiently identify industrial gas meter picture digits with 91.1% identification accuracy, faster inference speed, and higher accuracy than standard algorithms. The accuracy and practicality of the recognition can fulfill the needs of instrument data detection and recognition in industrial production, and it has a wide range of applications.

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Image pattern recognition in identification of financial bills risk management

Cited by 11 publications

References 18 publications

Data science and AI in FinTech: an overview

Data science and AI in FinTech: an overview

Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning

Multiple attention-based encoder–decoder networks for gas meter character recognition

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