Transforming BOL Images into Structured Data Using AI
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P112Keywords:
Bills of Lading (BOL), Optical Character Recognition (OCR), Natural Language Processing (NLP), AI in Logistics, Document Automation, Structured Data Extraction,, Intelligent Document Processing (IDP), Automated Data Capture, Logistics AI Solutions, Freight Document Processing, Machine Learning for Logistics, Supply Chain Automation, Text Recognition in Logistics, Digital Freight Documentation, AI-powered Data ExtractionAbstract
Most known in the financial and logistical domains, the bill of lading (BOL) is a legally enforceable documentation of transferred goods. It provides basic weight, commodities, shipper and consignee information, handling guidelines, cargo descriptions, and most crucially Processing BOLs is still challenging even if their unstructured approach, handwritten notes, many formats, and many degrees of intelligibility clearly show their value. Apart from time-consuming and expensive conventional manual data entering systems are prone to human errors producing inefficiencies, delays, and possibly financial losses. Artificial intelligence (AI) is altering BOL handling especially in optical character recognition (OCR) and natural language processing (NLP). OCR helps text extract from scanned or photographed BOL documents even in low quality images; NLP enhances understanding by means of locating, organizing, and classifying relevant facts. Artificial intelligence can learn to accept many BOL forms and handwriting styles by means of machine learning and pattern recognition, therefore considerably improving accuracy over time. Among the various benefits of automated systems running under artificial intelligence, few include higher efficiency, lower operational costs, and improved accuracy. Reducing human data input lets companies improve processing speeds, minimize mistakes, and enable best integration of structured BOL data into other systems such as enterprise resource planning (ERP), transportation management systems (TMS), and other logistics systems. Real-time data collecting and validation helps supply chain tracking, policy following, and decision-making in logistics firms as well. This paper presents a real case study illustrating the deployment of a BOL based artificial intelligence processing system. It looks at the key problems, artificial intelligence model building techniques, system integration, and the obvious impact on operational procedures. The results show how automation powered by artificial intelligence is changing logistics paperwork and guiding a more intelligent, scalable, reasonably cost products handling environment
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