JetStream Recognition

PATENTED CORE TECHNOLOGY

• Compex documents
• Preserves all transcripts
• Highest accuracy

WIDE SCOPE OF SCENARIOS



• Machine printed
• Handwritten text
• Barcodes & more

COMPREHENSIVE PDF CAPABILITIES

• Stores Smart PDFs with text layers
• Supporting PDF/A
• High efficiency archiving
  

AUTO LANGUAGE DETECTION



• Multiple language simultaneously
• Based on refined language models
• Pre-trained models

EASY DEPLOYMENT & INTEGRATION

• On-premises or cloud-based
• Java app or Docker
• Integrating seamlessly with gRPC API

Versatile JSON output format

• Output: JSON format
• Easy access to extracted data fields
• Seamless downstream processing
  

JetStream Recognition Add-Ons

What Is Document Recognition?

Document recognition, also known as document analysis or document understanding, is the process of automatically analyzing and understanding the content and structure of a document. It involves extracting meaningful information from documents, such as text, images, tables, and other elements, and interpreting their meaning and context. Document recognition techniques typically include optical character recognition (OCR) for extracting text from images, as well as techniques for layout analysis, document segmentation, and classification. Document recognition is widely used in various industries, such as finance, healthcare, and legal, to automate document processing tasks and improve efficiency.

OCR, Optical Character Recognition, is a technology that enables computers to recognize and extract text from images or scanned documents. OCR systems use algorithms and pattern recognition techniques to identify characters and transform them into machine-readable text. This technology is commonly used to convert physical documents, such as invoices, receipts, or printed text, into editable and searchable digital files. OCR has various applications, including document digitization, data entry automation, and improving accessibility for visually impaired individuals.

AI document recognition uses advanced techniques to analyze and understand entire documents, extracting meaningful information such as text, images, and tables while interpreting their context with normally a very high accuracy. It can combine different aspects such as OCR with document segmentation, layout analysis, and classification.

Traditional OCR, however, mainly focuses on recognizing and converting text from images into machine-readable form. It relies on pixel-based pattern recognition, identifying letters by analyzing their shapes. In contrast, AI-powered recognition uses neural networks and considers the entire document’s context, enabling more sophisticated comprehension and adaptability beyond basic text conversion.

1) Digitization and Document Management:

OCR enables the conversion of physical documents, such as paper files or scanned images, into editable and searchable digital text. This facilitates efficient document management, storage, and retrieval, reducing reliance on physical paperwork and enabling easy access to information.

2) Time & Cost Savings:

OCR automates data entry processes, eliminating the need for manual typing and reducing human error. This saves significant time and reduces labor costs associated with data entry tasks, enabling employees to focus on more value-added work.

3) Improved Accuracy & Efficiency:

OCR technology has advanced significantly, achieving high accuracy rates in text recognition. Automated data extraction with OCR reduces the risk of human error and improves data accuracy. It also speeds up processing times, allowing businesses to handle large volumes of documents quickly and efficiently.

4) Enhanced Searchability & Accessability:

By converting physical documents into searchable digital files, OCR enables users to easily locate specific information within documents. This improves efficiency in information retrieval and enables faster decision-making. Additionally, OCR helps make printed text accessible to individuals with visual impairments by converting it into speech or Braille.

5) Integration with Business Systems:

OCR can seamlessly integrate with various business systems, such as content management systems (CMS), enterprise resource planning (ERP) software, or customer relationship management (CRM) platforms. This enables streamlined workflows, data synchronization, and efficient data exchange between different systems.

6) Compliance and Regulatory Requirements:

Many industries have compliance and regulatory requirements related to document management and data processing. OCR technology helps organizations meet these requirements by ensuring accurate data capture, retention, and retrieval.

OCR technology offers significant advantages in terms of efficiency, accuracy, cost savings, and compliance. It has become an essential tool for businesses across industries, improving productivity, data management, and decision-making processes.