A semi-automatic method for document classification in the shipping industry
In the shipping industry, document classification plays a crucial role in ensuring that the necessary documents are properly identified and processed for customs clearance. OCR technology is being used to automate the process of document classification, which involves identifying important documents such as Commercial Invoices, Packing Lists, Export/Import Customs Declarations, Bills of Lading, Sea Waybills, Certificates, Air or Rail Waybills, Arrival Notices, Certificate of Origin, Importer Security Filings, and Letters of Credit. By using OCR technology, the shipping industry can improve accuracy and efficiency in document classification and streamline the customs clearance process. The aim of this study is to build a robust document classification system based on keyword frequencies. The research is carried out by analyzing Contract-Breach law documents available with IN-D. The documents were collected by scraping the Singapore Government Judiciary website. The database developed has 250 Contract-Breach documents. These documents are splitted to generate 200 training documents and 50 test documents. A semi-automatic approach is used to select keyword vectors for document classification. The accuracy of the reported model is 92.00 %.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDocument ClassificationOptical Character Recognition (OCR)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unified Container Shipping Industry Data From 1966: Freight Rate, Shipping Quantity, Newbuilding, Secondhand, and Scrap Price
We construct a new unified panel dataset that combines route-year-level freight rates with shipping quantities for the six major routes and industry-year-level newbuilding, secondhand, and scrap prices from 1966 (the beg…
DescriptiveText classification in shipping industry using unsupervised models and Transformer based supervised models
Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the perfor…
ClassificationSelf-Learningtext-classificationText Classification+2Industry Dynamics with Cartels: The Case of the Container Shipping Industry
I investigate how explicit cartels, known as ``shipping conferences", in a global container shipping market facilitated the formation of one of the largest globally integrated markets through entry, exit, and shipbuildin…
counterfactualLogic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification
The shipping industry is an important component of the global trade and economy, however in order to ensure law compliance and safety it needs to be monitored. In this paper, we present a novel Ship Type classification m…
SHACL-SKOS Based Knowledge Representation of Material Safety Data Sheet (SDS) for the Pharmaceutical Industry
We report the development of a knowledge representation and reasoning (KRR) system built on hybrid SHACL-SKOS ontologies for globally harmonized system (GHS) material Safety Data Sheets (SDS) to enhance chemical safety c…