High-quality visual labeling for multimodal AI, OCR workflows, and computer-vision model improvement.
Into23 supports multimodal AI teams that need more than basic box drawing. We provide bounding boxes, segmentation, OCR validation, damage and defect labeling, and structured visual QA with the domain judgment required for production data quality.
Starting from $0.10 per image · OCR validation from $0.50 per page · Final scope depends on ontology complexity, review tier, and specialist validation requirements.
We deliver the core object-labeling layers needed for detection, localisation, and scene-understanding workflows.
Human validators check printed and visual text across scripts so OCR systems learn from cleaner ground truth.
Annotation can include severity, type, and region labeling for insurance, inspection, and quality-control use cases.
Where images include language content, native-speaking validators help catch script-level and context-level OCR issues.
We use review layers, agreement checks, and client calibration to keep annotation quality stable at scale.
Outputs are aligned to the model objective, whether that is document AI, visual search, VQA, inspection, or multimodal reasoning.
We align on annotation type, class definitions, edge cases, and quality thresholds based on the model objective.
Pilot examples and reviewer feedback are used to reduce ambiguity before large-scale production begins.
Teams complete labeling with review loops, consistency checks, and issue logging across the dataset.
You receive clean visual labels, QA observations, and practical guidance for future annotation rounds.
Into23 supports visual annotation workflows where multilingual text, OCR validation, and structured QA matter as much as the image labels themselves.
Into23 handles bounding boxes, polygon segmentation, OCR validation, damage and defect labeling, and structured visual QA. We support document AI, visual search, VQA, inspection, and multimodal reasoning workflows.
Images often contain text in multiple scripts and languages. Native-speaking validators catch script-level and context-level errors that generic annotators miss, producing cleaner ground truth for OCR systems.
Yes. We can include severity, type, and region labeling for insurance, inspection, and quality-control use cases, with schemas aligned to your specific model objective.
Get a custom quote for your image recognition annotation project. Our team typically responds within 24 hours.