• Why IRONSCALES
    OUR VALUE
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    Press: New Research

    New Osterman study reveals orgs with high confidence still lack capabilities against QR code attacks

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    Learn how Webhelp saved over 450 SecOp hours and is protecting 36,000 mailboxes

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    IRONSCALES earns 'Fastest Growing Email Security Company' for 3rd straight year

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    USE CASE
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      Stop advanced attacks like BEC, VEC, and VIP impersonation

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    FEATURED
    Email Security in the Age of AI
    Email Security in the Age of AI

    Understand the role of AI in fortifying defenses against evolving threats.

    QR Code Phishing
    QR Code Phishing

    QR codes serve as a new bypass method to evade detection.

    The ABCs of MFA
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    Phishing Prevention
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What is OCR Deep Learning?

OCR Deep Learning uses AI algorithms to enhance OCR accuracy by training on large datasets, improving text recognition even in complex scenarios.

OCR Deep Learning Explained

Optical Character Recognition (OCR) is a technology used to convert various types of documents, such as scanned paper documents or images, into editable and searchable text data. Traditional OCR systems rely on predefined rules and templates to recognize text characters within images or documents.

OCR Deep Learning represents an advanced approach to OCR technology that utilizes deep learning algorithms, a subset of artificial intelligence (AI), to enhance text recognition and data extraction capabilities. Unlike traditional OCR, which relies on predefined rules, OCR Deep Learning models are trained on large datasets to recognize and interpret text more effectively, even in complex scenarios.

 

How OCR Deep Learning Works

OCR Deep Learning operates by leveraging neural network architectures to analyze images and extract textual information. The process involves a few key steps:

  • Data Collection: Large datasets of images containing text are gathered for training purposes.
  • Model Training: Deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), are trained on these datasets to learn how to accurately recognize and interpret text.
  • Feature Extraction: The trained model identifies patterns and features within images that correspond to text characters.
  • Text Recognition: Once trained, the model can accurately recognize and transcribe text from images or documents, even in challenging conditions.

 

Examples of OCR Deep Learning

  • License Plate Recognition for Parking Lot Management and Toll Collection: OCR Deep Learning tech is used for license plate recognition in parking lots and toll collection systems. Cameras capture vehicle images, and OCR algorithms extract alphanumeric characters from license plates. This data is used for parking lot management and toll collection.
  • Document Digitization for Data Entry and Archival Purposes: OCR Deep Learning converts physical documents into digital formats, saving time and increasing efficiency. It allows digitized documents to be stored electronically, enhancing organizational efficiency and compliance.
  • Text Extraction from Images for Inventory Management and Document Processing: OCR Deep Learning technology is used to extract text information from images in various industries. It can extract product information, serial numbers, and other data from images of product labels, packaging, or inventory lists for inventory management. It can also extract text from images of documents like handwritten notes, receipts, or medical forms for document processing.

 

What Role Does OCR Deep Learning Play in Email Security?

In email security, OCR Deep Learning plays a crucial role in detecting and preventing phishing attacks. By analyzing email content, including images and attachments, OCR Deep Learning algorithms can identify suspicious text patterns and potential phishing attempts, helping to protect users from malicious emails.


How to Identify and Protect Against OCR Deep Learning Attacks

To identify and protect against OCR Deep Learning attacks, organizations can implement the following measures:

  • Regularly update OCR Deep Learning models with new training data to improve accuracy and resilience.
  • Implement anomaly detection techniques to identify abnormal text patterns or behaviors indicative of potential attacks.
  • Employ multi-layered security controls, including email filtering and endpoint protection, to mitigate the risk of OCR Deep Learning-based threats.


IRONSCALES Uses OCR Deep Learning in Attack Prevention

IRONSCALES provides advanced email security solutions that leverage OCR Deep Learning to defend against evolving threats. By continuously monitoring email content and attachments, IRONSCALES detects and blocks phishing attempts, business email compromise (BEC) attacks, and other email-borne threats that may utilize OCR Deep Learning techniques.

Additionally, IRONSCALES offers real-time threat intelligence and incident response capabilities to help organizations proactively defend against OCR Deep Learning-based attacks.

Learn more about IRONSCALES advanced anti-phishing platform here. Get a demo of IRONSCALES™ today!  https://ironscales.com/get-a-demo/

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Featured Content

AI in Email Security

This comprehensive Osterman Research study explores the evolving landscape of AI-driven threats and innovative solutions implemented to stay ahead.

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Gartner® Email Security Market Guide

This guide gives email security experts an exclusive access to Gartner® research to ensure their existing solution remains appropriate for the evolving landscape.

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Defending the Enterprise from BEC

Data shows organizations deploy defense-in-depth approaches ineffective at addressing BEC attacks. Discover truly effective strategies in this report.

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