As of last year we had OCR'd more than 19 billion seconds of global television news totaling 294 billion words yielding almost 2 petabytes of JSON. One of the most common questions we get is why we use classical advanced OCR instead of just using LMM models for this OCR, especially the tremendous success we […]
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The Role of Classical OCR in Analyzing Global Television News
2w ago
October 14, 2026
1 min read
📰 GDELT Historical Archive
📋 Key Takeaway
The article discusses the use of classical OCR technology for processing global television news instead of LMM models, highlighting the extensive amount of data processed. While not directly related to Iran, the implications of technology in media analysis can impact how news from Iran is interpreted and disseminated.
🔍 Quick Context Guide
💡 Bottom Line:
The article highlights the importance of OCR technology in shaping media narratives, which can indirectly affect perceptions of Iran.
📰 What Happened
The article discusses the use of classical Optical Character Recognition (OCR) technology in processing a vast amount of global television news data, emphasizing its advantages over language model-based methods.
- Over 19 billion seconds of television news have been OCR'd, resulting in 294 billion words.
- The processed data amounts to nearly 2 petabytes in JSON format.
💡 Why It Matters
🇮🇷 For Iran: The analysis of global television news can influence how Iranian news is perceived and reported internationally.
🌍 Regional: Improved media analysis can enhance understanding of regional issues, including those affecting Iran.
🌐 International: The use of advanced OCR technology may affect how international audiences consume and interpret news from Iran and other regions.
📚 Background
Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents or images of text, into editable and searchable data.
Media technology advancements
Impact of technology on news dissemination
📡 Source:
NEUTRAL
📊 Confidence:
70%
The source appears to provide a technical overview without overt bias, focusing on the capabilities of OCR technology.
🏷️ Entities Mentioned
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Translated from the original and edited for English readers. View original source →
Translation confidence: 100%
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