Panoramic Point Cloud OCR for Distortion-Reduced Text Localization
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Solution Overview
Problem
Existing systems fail to accurately identify and index textual information in physical environments, lacking location data and searchable context for text within point cloud scans, leading to manual annotation processes and poor text recognition.
Innovation Solution
A system that transforms panoramic point cloud images into alternate representations to reduce distortion, performs OCR to identify text, constructs text labels with location data, and enables searchable text searches and high-contrast visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If panoramic point cloud images are used to capture physical environments, then comprehensive spatial data is obtained, but text recognition accuracy deteriorates due to distortion and lack of context
Solution Approach 1:
The system segments the panoramic point cloud image into multiple localized images captured from different viewpoints. Each localized image provides a clearer, less distorted view of specific text regions, enabling accurate OCR while maintaining comprehensive spatial coverage through the combination of multiple segmented views.
Solution Approach 2:
The system introduces an intermediary process that transforms 3D point cloud data into 2D localized images with reduced distortion. This intermediary representation serves as a bridge between the comprehensive spatial data and accurate text recognition, allowing OCR to operate on optimized 2D projections while preserving 3D location information.
2Reliability
If manual annotation processes are used to identify text locations, then text can be tracked, but productivity deteriorates due to time-consuming manual intervention
Solution Approach 1:
The system implements self-service by enabling automatic text detection and location identification through OCR processing of localized images. The system autonomously extracts text, determines its 3D coordinates, and annotates the point cloud data without requiring manual intervention, thereby maintaining reliable text location tracking while dramatically improving productivity.
Solution Approach 2:
The system replaces the mechanical manual annotation process with an automated computational system. Instead of human operators manually identifying and marking text locations, the system uses image processing algorithms and OCR to automatically detect, recognize, and locate text elements within the point cloud data.
3Loss of information
If text is overlaid directly on panoramic point cloud images, then location information is preserved, but text clarity deteriorates due to image distortion
Solution Approach 1:
The system transitions from displaying text directly on distorted 3D point cloud visualizations to projecting text onto 2D localized images with reduced distortion. This dimensionality change allows text to be overlaid on clearer 2D representations while the system maintains correspondence with 3D spatial coordinates, preserving location information through coordinate mapping between dimensions.
Data Source
AI summary
A computing system may include an image access engine configured to access a panoramic point cloud image of a physical environment. The computing system may also include an environment location-aware text engine configured to transform the panoramic point cloud image into an alternate representation that reduces distortion in the panoramic point cloud image and perform an optical character recognition (OCR) process on the alternate representation to determine text in the panoramic point cloud image. The environment location-aware text engine may further be configured to construct text labels to track the text determined in the panoramic point cloud image and support text searches for the physical environment through the text labels.