The differences between OCR & text moderation

Even though OCR and text moderation may seem like the same thing, they are not. OCR and AI text moderation models both serve different purposes however, they can be complementary when used together in content moderation workflows. Here are the main differences between the two types of models:

OCR

OCR is primarily focused on extracting text from images or scanned documents. It converts text that appears in images into machine-readable text that can be processed and analysed. OCR is not inherently designed to understand the meaning or context of the extracted text. It´s main goal is to accurately recognise and covert text from images into a digital format. Regarding content moderation, OCR is often used to analyse text content within images or scanned documents, enabling platforms to identify and filter out inappropriate or harmful text-based content.

AI Text Moderation Models

AI text moderation models are designed to analyse and moderate text-based content, typically in the form of user-generated text submissions such as comments, messages, or posts. These models utilise natural language processing techniques and machine learning algorithms to understand the meaning, context, and sentiment of text. Similarly, these models can detect various types of inappropriate content including hate speech, harassment, spam, profanity, and misinformation. They can also consider factors such as user context, historical behaviour, and community guidelines when making moderation decisions. Unlike OCR, AI text moderation models do not rely on image processing to extract text they can directly analyse text data in digital format.

In sum, while OCR focuses on extracting text from images, AI text moderation models focus on analysing and moderating text-based content. However, in content moderation workflows, these technologies can be integrated to provide comprehensive moderation capabilities. For example, OCR can be used to extract text from images, which can then be analysed by AI text moderation models to identify and filter out inappropriate content. This combined approach helps platforms address a wider range of content moderation challenges effectively.