Quickly scan, digitize, and extract meaningful insights from supply chain documents like invoices, purchase orders, bills of lading, and customs documents. Automate the process of analyzing those documents instead of manual data entry.
Business Impact: process more documents in less time, decrease processing costs, increase productivity, and reduce errors resulting from manual processing.
Understand the Use-case under 5 minutes
Video (3 minutes)
IBM Business Automation Content Analyzer in action including the creation of a document class and its title, classification words, and keys in an easy-to-use cloud service configured for business analysts and powered by AI
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Article (2 minutes)
by applying AI data extraction solutions, logistics companies can automate backend processes (e.g. extract data from invoices, bill of ladings) to improve efficiencies and turnaround times and reduce costs and errors
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Article (5 minutes)
Comparing traditional, OCR, and smart AI techniques for invoice processing
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article (10 minutes)
A detailed guide on the value AI brings to document processing for logistics companies, and how to overcome challenges when scaling workflows with machine learning
ReadCase studies, Organizational Aspects, Return on Investment examples
Article (10 minutes)
Quantifying the economic cost of not using an automated document processing technology for your supply chain operations
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Article (7 minutes)
Examining four areas forwarders need to consider when weighing up a generalist document automation solution against an industry specialist one
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Article (7 minutes)
Pros an Cons for Building vs Buying, touching on factors like: speed, total cost of ownership, and integrations
ReadMore details on the technical aspects of the use-case
Video (21 minutes)
Highlighting Document AI which is a cloud service from Google that seeks to extract information from documents using machine learning algorithms, the video shows the challenges and capabilities.
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Machine learning service that use OCR to automatically extract text, handwriting, and text data from scanned documents
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Azure Form Recognizer applies advanced machine learning to accurately extract text, key-value pairs, tables, and structures from documents
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
AWS Textract, AWS Comprehend, Azure Form Recognizer, and Google Document AI are codeless cloud services that can help you extract insights from different types of documents using ML-based OCR and NLP
Article (8 minutes)
How to automate the document processing in the logistics industry. The article also shows how to integrate it with a centralized workflow. Includes reference architecture using AWS
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Article (22 minutes)
Comprehensive guide for structured key-value pair information extraction from invoices, this review contains the state of the art that tackles the intelligent data extraction problem
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GitHub repo
Deep neural network to extract intelligent information from invoice documents
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Article + Github Repo
A comprehensive report showing the work made by google in their latest paper, explaining all details from the dataset, methodologies, and achieved results. [GitHub repo]
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Data Sets you can use to build Demos, POCs, or test Algorithms
200 images of restaurant receipts. Each receipt is shown in its entirety and includes business name, business address, cost, itemized items, subtotal, tax (if applicable), and total
Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance.
Images provided by the United States National Archive and FamilySearch. The goal of this data is to capture relationships between text/handwriting entities on form images
Off-the-Shelf Products using AI for Intelligent Document Processing
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