Serial Number Recognition Research Articles

Serial Number Recognition of Ceramic Membrane Based on End-to-end Deep Learning

Serial Number Recognition of Ceramic Membrane Based on End-to-end Deep Learning

Multi-Currency Integrated Serial Number Recognition Model of Images Acquired by Banknote Counters.

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used.

An attention-based network for serial number recognition on banknotes

The serial number recognition (SNR) on banknotes is essential for currency circulation. The performance of the existing SNR methods is significantly influenced by character segmentation, which is challenging due to uneven illumination and complex background. In this paper, we apply deep learning techniques to SNR by proposing an attention-based network, which can be end-to-end trained to avoid the problem of character segmentation. The proposed framework contains two parts: rectification and recognition. First, the rectification network, which can be trained in a weakly supervised manner without additional manual annotations, is built to automatically rectify the tilted and loosely-bounded images and reduce the difficulty of recognition. Then, the recognition network, an attention-based sequence model, recognizes serial number characters from the rectified image holistically, rather than segmenting and recognizing individual characters. To address the problem of complex textures on banknotes, we integrate the deformable convolution into the recognition network, which adaptively focuses on the character regions by using flexible receptive fields to accurately extract optimal character features, while ignoring redundant background information. Extensive experiments conducted on CNY, KRW, EUR and JPY banknotes, demonstrate that the proposed method achieves higher accuracy than the existing methods.

Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

Banknote serial number recognition using deep learning

In this paper, we recognize serial numbers on banknotes using deep learning. The samples used in this paper are digital images which have been preprocessed with data labelling and data augmentation, e.g., scaling transformation, etc. The algorithms based on deep learning are proposed and have the stability for serial number recognition with complex backgrounds. In this paper, a pipeline of deep neural networks is established for the recognition of banknote serial numbers. Because high reliability is more important than accuracy in financial applications, DenseNet is set forth as the primary classifier, the scaling transformation of SegLink is put forward to locate the characters, the detection rate is up to 95.80%. A convolutional neural network with residual attention model is proposed for serial number recognition, the precision is up to 97.09%.

SNRNet: A Deep Learning-Based Network for Banknote Serial Number Recognition

The banknote serial number recognition (SNR) plays an important role in the banking business and attracts much attention recently. However, most of the existing SNR methods take character segmentation and character classification as two separate steps, so that the accuracy of SNR heavily relies on the character segmentation, which is a challenging problem due to complicated background and uneven illumination. In this paper, the SNR is cast into a sequence prediction problem, which integrates such two steps into a unified network, and we propose a deep learning-based serial number recognition network, which can be trained end-to-end to avoid the preliminary character-segmentation with three steps as follow. First, the improved convolutional neural networks are employed to extract the feature sequence of the input image. Second, the feature sequence is used as an input to the bidirectional recurrent neural networks (BRNNs), where the character segmentation is not required. Finally, the label recognition is implemented using the connectionist temporal classification to decode the BRNNs’ output. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in both accuracy and efficiency: it achieves character and serial number recognition of the renminbi (RMB) with accuracies 99.96% and 99.56%, respectively.

Low-Quality Banknote Serial Number Recognition Based on Deep Neural Network

Low-Quality Banknote Serial Number Recognition Based on Deep Neural Network

Blockchain for tracking serial numbers in money exchanges

SummaryMoney exchange is one of the most common day‐to‐day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real‐time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea.

Machine Learning-Based Fast Banknote Serial Number Recognition Using Knowledge Distillation and Bayesian Optimization.

We investigated a machine-learning-based fast banknote serial number recognition method. Unlike existing methods, the proposed method not only recognizes multi-digit serial numbers simultaneously but also detects the region of interest for the serial number automatically from the input image. Furthermore, the proposed method uses knowledge distillation to compress a cumbersome deep-learning model into a simple model to achieve faster computation. To automatically decide hyperparameters for knowledge distillation, we applied the Bayesian optimization method. In experiments using Japanese Yen, Korean Won, and Euro banknotes, the proposed method showed significant improvement in computation time while maintaining a performance comparable to a sequential region of interest (ROI) detection and classification method.

A hybrid convolution network for serial number recognition on banknotes

As the sole identity of banknote, serial number has played a crucial role in monitoring the circulation of currencies. Serial number recognition plays an important role in financial market, which requires fast and accurate performances in real applications. In this paper, a hybrid convolution network model has been proposed, in which a dilated-based convolution neural network is employed to improve the recognition accuracy and a quantitative neural network method is developed to speed up the identification process. In dilated-based convolution neural network, the convolution layer and the pooling layer have been replaced by dilated convolution, which can reduce the computation cost. The quantitative neural network based method quantizes the weight parameters to an integer power of two, which transforms the original multiplication operation to a shift operation and can greatly reduce the time. The proposed model was examined and tested on four different banknotes with 35,000 banknote images including RMB, HKD, USD and GBP. The experimental results show that, the proposed model can efficiently improve the recognition accuracy to 99.89% and reduce the recognition time to less than 0.1 ms, and it outperforms the other algorithms on both recognition accuracy and recognition speed.

A novel character segmentation method for serial number on banknotes with complex background

The serial number recognition has played a crucial role in financial market. And the character segmentation is the most important part for serial number recognition. In this paper, in order to deal with the noises and complex textures of the banknotes, we propose a novel method which is composed of a hybrid binarization algorithm (HybridB) and an adaptive character extraction algorithm (ACE). The HybridB algorithm utilizes both the global information of the whole image as well as the local information of the pixels which can eliminate the interferences of the noises and background. The ACE algorithm adjusts the boundaries of each character by the local contrast average value information and the distance between its gravity and center, and a decreasing step strategy is also employed to optimize the boundaries to get more accurate segmentation results. To validate the performance of the proposed method, we take experiments on the datasets of RMB, GBP and USD. The binarization results and character extraction results show that our proposed method outperforms the other state-of-the-art algorithms in most cases.

A Survey on Banknote Recognition Methods by Various Sensors.

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.