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%.

Automated bank cheque verification using image processing and deep learning methods

Driven by the internet-based advanced information technologies and logistics channel improvement, the cross-border e-commerce industry keeps an increasing trend in Chinese industrial market. Blockchain, as an empowered technology, contributes to the management innovations for industrial sectors. The blockchain technology, due to its transparency, visibility, and dis-intermediation characteristics, helps to improve operations management of cross-border e-commerce supply chain by innovative industrial applications. However, practical applications of the blockchain technique-enabled cross-border e-commerce sector are still in their infancy and still at the proof-of-concept stage. This paper presents a systematic review on blockchain-enabled cross-border e-commerce supply chain management by employing a bibliometric data-driven analysis. All relevant publications from the Web of Science database from 2013 to 2021 were collected as the research samples. Besides, the VosViewer is adopted to conduct the network and co-word study by visualizing collaborative relationships of sampled literatures. Results show that the blockchain technique has substantial applications in the field of cross-border e-commerce supply chain, whose contributions mainly focus on cross-border e-commerce platform, supply chain operations, and data governance and information management. Academic researchers and industrial managers can promote innovative management practices in cross-border e-commerce supply chain by adopting blockchain. Moreover, we hope this study serves as a future direction for both researchers and engineers on leveraging blockchain to improve the supply chain management performance of the cross-border e-commerce.

/pdf/blockchain-enabled-cross-border-e-commerce-supply-chain-2w39bmvq.pdf

Blockchain-Enabled Cross-Border E-Commerce Supply Chain Management: A Bibliometric Systematic Review

This paper analyzed the important role of pattern matching in the Web intrusion detection and problems proposed right now,and then surveyed the Aho-Corasick algorithm based on automata and some related algorithms based on Aho-Corasick algorithms.Through experiments on a variety of multi-pattern matching algorithm for integrated comparison,predicted the developing trend in the end.

Research on multi-pattern matching algorithms based on Aho-Corasick algorithm

There is a solid requirement for the real-time ordering of enormous measures of information streaming at the rate of 10GB/s or more. This information should be scanned for designs and the query items are time-basic in fields as different as security reconnaissance, monetary services including stock exchanging, checking the basic well-being states of patients, atmosphere notice frameworks, etc. Here, the file will be required to age-off in a little time and thus will be of limited size. Notwithstanding, such situations can't endure any infringement of indexing latency and severe pursuit reaction times. Likewise, future greatly parallel (multicore) structures with capacity class recollections will empower rapid in-memory constant indexing, where the index can be totally put away in a high limit storage class memory. As the web is growing, it means a number of documents on the web are also growing so index size will also grow. The approach would be to partition a single large search index into smaller partitions and assigned to each node in the cluster.  Later when a search request comes each node in the cluster will perform a search on the local index for each query term and send the result back to the central machine, which will later combine search results from all nodes in the cluster and then send it to the user. In this paper also comparative analysis of rabin-karp and Knuth-Morris-Pratt and Boyer-Moore algorithm has been done based on the execution time of the search pattern. An experimental result of the algorithm is presented in this paper, based on the result it has been concluded that KMP algorithm is better to use in MLIR framework.

Analysis Of Pattern Matching Algorithms Used For Searching The Patterns In MLIR Framework

Multi-pattern matching refers to the search for multiple patterns in a given text at the same time. This matching on FPGA is expected to scale with the number of patterns in hardware consumption. In this paper, we propose a matching architecture that compares the prefixes of multiple patterns with the prefix of the matching window in parallel. The comparison will continue with the body of each pattern if the corresponding prefix is matched. This architecture is called the prefix-based multi-pattern matching architecture. Our implementation on FPGA shows that the proposed matching architecture achieves much higher performance than the implementation on CPU, while the hardware cost is low.

Prefix-Based Multi-Pattern Matching on FPGA

Multi-pattern matching is a matching algorithm that searches multiple pattern parallel in the given text. According to problems of the existing multi-pattern matching algorithm, the paper puts forward a multi-pattern matching algorithm based on double hash. The improved algorithm could reduce the number of comparison each matching by two hashes, whose hash table used to store patterns is a two-dimensional array of pointers. Each node represents head node of a single list in the two-dimensional array, all nodes of a single list with the same length and prefix hash of pattern string. Through the hash physical structure, text substring needs also two hashes in each matching to find the corresponding hash. It needs complete matching if finding node with the same value of hash of two times in this physical structure. Otherwise, the matching pointer of text string moves to the right quickly by jump distance of BM2 algorithm based on the multi-pattern strings. The experiment shows that the time performance of TH_MPMA algorithm is better than that of AC_BM2 algorithm under the same test environment.

Research and improvement of a multi-pattern matching algorithm based on double hash

Large-scale and high-speed networks cause rapid increase in network flow, resulting in the omission of reports of the intrusion detection based on the string pattern matching algorithm. In this paper, a multi pattern matching algorithm C_MPM based on characteristic value is proposed. In the algorithm, the rule base is classified two times according to the length and the characteristic value, and obtains various pattern string queues, which can reduce the number of string matches that each text string needs to match each time; Secondly, the characteristic values of text strings of different lengths in the matching window are calculated in parallel, and the pattern string queues in the rule base are searched according to the length and the characteristic values of the text string, so that the full mode pattern matching can be carried out. The experimental results show that the time performance of the matching algorithm C_MPM is better than that of the AC_BMSH2 algorithm.

Research of multi-pattern matching algorithm based on characteristic value

The performance improvement of multi-pattern matching algorithm has an important impact on intrusion detection. For the two main disadvantages of the WM algorithm, one is to perform a pattern string filter only through the suffix table, and the other is that the average moving distance of the matching window on the text string is too small. Based on above theory, this paper proposes an improved WM algorithm I_WM. Firstly, the I_WM algorithm sorts all pattern strings according to the suffix characters of the pattern string matching window; then it calculates the hash value of the suffix character block in the text string matching window, searches the suffix table according to the hash value, determines the address range of the participating pattern strings, and filters the pattern strings that need to be matched for the first time; then it calculates the hash value of the prefix character block in the text string matching window, searches for the corresponding pattern strings in the prefix table according to the hash value and then traverses them, only the pattern strings within the address range specified by the suffix table need to be matched, which is the second filtration of pattern strings. By filtering twice, the number of pattern strings to be matched each time is greatly reduced. In addition, the text string matching window moves backward for the larger character block jump distances calculated by the BM and BMHS2 algorithms. The experimental test results show that under the same test environment, the I_WM algorithm has a certain degree of improvement over the WM algorithm in matching performance.

Research and Improvement of a WM Multimode Pattern Matching Algorithm

The pattern matching algorithm has important influence on the performance of intrusion detection system. Firstly, this paper analyzes matching principles of some traditional single-mode matching algorithms including KR, BM2 and frequency statistics algorithm. BM2 algorithm has a larger rightward deviation distance when mismatching and KR algorithm effectively reduces the number of matches by comparing the hash values of strings and word frequency statistics algorithm can find the mismatch character as soon as possible. The article puts forward the improved single-mode pattern matching algorithm Based on the analysis above. The improved algorithm firstly uses the frequency statistics method, the character of the lowest frequency in the pattern string is matched with the corresponding character of the text string; if matching, KR algorithm is used to compare the hash value of pattern string and text substring; if it is still matching, the BM2 algorithm is used to match. In the above matching processes, if there is any mismatching, the algorithm uses computed rightward deviation distance of BM2 algorithm to move the pattern string. Test experiments show that improved algorithm reduces the number of character matchings and the matching performance is improved in the same circumstance.

Research of a single-mode pattern matching algorithm based on word frequency and hash

The improvement of the time performance of pattern matching algorithm mainly lies in reducing the number of character comparisons and increasing the distance of the matching window moving to the right when mismatch. In this paper, it adopts the second idea of improvement. The paper first analyzes KMP algorithm and its improved one, and then introduces BMHS2 algorithm. The distance of moving to the right of two improved algorithms is calculated when mismatch occurs respectively, and then proposes an improved algorithm based on the combination of improved KMP and BMHS2. The idea of this matching algorithm is that the overall matching is carried out from left to right, and every time the matching is carried out from left to right. When the text substring in matching window is mismatched with the pattern string, the larger jump distance of the I_KMP and BMHS2 is adopted to move the matching window to the right. Finally, two experiments to compare the time performance of the three algorithms above are carried out. The result shows that in the same experiment condition, compared to improved KMP and BMHS2, the time performance of improved pattern matching algorithm I_KMP_BMHS2 improved to some extent.

Research of Pattern Matching Algorithm Based on KMP and BMHS2

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