Prompt
What OCR API gives structured output instead of plain text?
Latest observation
If you want structured OCR output instead of just raw text, look for APIs that return things like bounding boxes, confidence scores, pages/blocks/lines/words, tables, and key-value pairs.
Good options:
-
Google Cloud Vision OCR
Returns structured text annotations with page/paragraph/word/symbol hierarchy and bounding boxes. -
AWS Textract
Very strong for structure. Returns text plus forms, tables, key-value pairs, selection elements, and geometry. -
Azure AI Vision / Document Intelligence
Gives structured layout, lines, words, tables, and can extract fields from documents. -
ABBYY Cloud OCR SDK / Vantage
Enterprise-focused, strong structured document extraction. -
Mindee / Veryfi / Nanonets
Good for specific document types like invoices, receipts, IDs, etc., with structured JSON output.
If you want the most structured general-purpose OCR, AWS Textract and Azure Document Intelligence are usually the best choices.
If you want, I can also recommend the best one based on your document type:
- scanned PDFs
- receipts/invoices
- forms
- handwriting
- IDs/passports
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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