A Computer Use Agent（CUA） processes dense on-screen text by combining OCR, layout analysis, and semantic interpretation. When faced with dashboards, logs, analytics tables, or documents with long paragraphs, the CUA first extracts text using OCR. It then divides the text into regions—headers, rows, labels, paragraphs—based on visual boundaries and alignment. This regional understanding helps the agent treat structured data differently from narrative text, improving the accuracy of downstream actions such as searching, selecting, or interpreting content.
Once text is extracted, the CUA uses semantic reasoning to interpret meaning and decide what actions to take next. For example, if asked to “find the error message,” the agent may look for keywords like “error,” “fail,” or “exception.” For tabular data, the CUA may look for column headers and cell values, then filter rows that match a given condition. In dense paragraphs, it may search for relevant sentences or highlight key terms before performing follow-up actions like copying, summarizing, or navigating linked elements.
Developers can significantly enhance dense-text processing by storing embeddings of text blocks or UI regions in a vector database such as Milvus or Zilliz Cloud. When the CUA is asked to locate a specific concept—like “compliance notice” or “monthly revenue”—it can query the vector store to find similar content on screen. This approach is particularly helpful when terminology varies across applications or when dense text is too visually cluttered for simple keyword search. Vector-based retrieval adds semantic depth to the CUA’s text-understanding capabilities.