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Description
DeepTagger is an innovative, no-code platform that utilizes artificial intelligence to transform various document types, such as PDFs, images, and Word files, into organized and actionable data using a user-friendly "highlight-and-label" system. Users simply upload their documents, select the relevant data points, and train the model through examples instead of relying on rigid templates, after which they can execute predictions, export their findings, and improve accuracy. The platform is designed to manage intricate structures, such as line items within invoices and tables within other tables, while also accommodating scanned documents and low-resolution images thanks to its powerful optical character recognition (OCR) capabilities. Additionally, DeepTagger includes functionalities for splitting multi-document PDFs, understanding intent and context, and position-aware extraction to differentiate repeated phrases for more precise data retrieval. Its pricing model is based on usage and offers a free tier for processing up to 200 documents, while higher subscription levels provide access to enhanced features, including batch prediction, nested schemas, priority support, a multi-tenant architecture, and compliance suitable for enterprise needs. Overall, DeepTagger stands out as a versatile solution for those looking to streamline their document processing and data extraction workflows.
Description
Extracting information from both scanned and digital documents is essential for modern businesses. Regardless of the layout or complexity of the documents, it is possible to convert them into an organized and machine-readable format. This automation of document processing allows for the efficient handling of all types of business documents. By transforming scanned and digital materials into a structured format, organizations can utilize this cleaned data for various downstream processes, whether that means storing it in a database or exporting it to a spreadsheet. This solution surpasses the capabilities of basic OCR and standard document parsing, as simply extracting plain text is often inadequate for many applications. Instead, it is crucial to convert text and data embedded within documents of any size into structured information. This approach not only enhances the scale and efficiency of business operations but also automates data extraction, resulting in immediate improvements in workflow. By processing a significantly larger volume of documents, businesses can reduce the need for additional personnel dedicated to document management and minimize the risk of human error. Ultimately, this transformative capability streamlines operations and drives productivity across the organization.
API Access
Has API
API Access
Has API
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
DeepTagger
Country
United States
Website
deeptagger.com
Vendor Details
Company Name
Quantxt
Founded
2013
Country
United States
Website
quantxt.com
Product Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction
Product Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction