Executive Summary
Computer vision — the discipline of enabling machines to interpret and understand visual information — is emerging as the foundational technology layer for a new generation of assistive devices serving blind and low-vision users. The global computer vision market in assistive technology is projected to reach $4.2 billion by 2027, growing at a CAGR of 34% from a 2023 base of approximately $980 million. This growth is being driven not by incremental improvements to existing products, but by a fundamental architectural shift: the migration of inference workloads from cloud servers to on-device neural processing units (NPUs) embedded in consumer-grade silicon.
This report examines the technical underpinnings of that shift, the competitive dynamics among silicon vendors, and the application categories where computer vision is delivering measurable impact for the 2.2 billion people globally living with vision impairment.
The Architecture Shift: From Cloud to Edge
For most of the 2010s, computer vision applications for assistive technology were cloud-dependent. A device would capture an image, transmit it to a remote server, run inference on GPU clusters, and return a result — a process that introduced latency measured in seconds and created hard dependencies on network connectivity. For a navigation device operating in real time, this architecture was fundamentally unsuitable.
The introduction of dedicated neural processing units in mobile and embedded silicon has changed the calculus entirely. Apple's Neural Engine, first introduced in the A11 Bionic chip in 2017 and now delivering over 38 TOPS in the A18 Pro, enables real-time on-device inference for tasks including object detection, depth estimation, and optical character recognition. Qualcomm's Hexagon NPU, integrated into the Snapdragon 8 Elite platform, delivers 45 TOPS at a thermal envelope compatible with handheld devices. MediaTek's Dimensity 9400 offers comparable performance at a lower cost point, making capable edge AI accessible in mid-range hardware.
The practical consequence for assistive technology developers is significant: a device running a MobileNetV3 or EfficientDet-Lite model on a Snapdragon 8 Elite can classify objects in a live camera feed at 60 frames per second with under 20 milliseconds of end-to-end latency — entirely offline, with no cloud dependency.
Core Application Categories
Real-Time Obstacle Detection and Navigation
The most safety-critical application of computer vision in assistive technology is obstacle detection for pedestrian navigation. Modern systems combine depth sensing (LiDAR or structured light) with camera-based semantic segmentation to distinguish between obstacle types and assess traversability. A parked scooter on a sidewalk, a low-hanging branch, and a wet floor sign each require different user guidance responses — a distinction that pure depth sensing cannot make but semantic segmentation can.
Glidance's navigation stack uses a custom-trained EfficientDet model fine-tuned on a proprietary dataset of over 2 million annotated pedestrian-environment images, including edge cases such as construction scaffolding, outdoor dining furniture, and mobility scooters. The company reports a false-negative obstacle detection rate below 0.3% in controlled testing — a figure that will need independent clinical validation before regulatory bodies accept it as a safety claim.
Scene Description and Environmental Awareness
Beyond navigation, computer vision enables rich environmental awareness: reading menus, identifying products on store shelves, recognizing faces, interpreting transit signage, and describing unfamiliar spaces. Microsoft's Seeing AI application — available on iOS and Android — uses Azure Cognitive Services models to deliver real-time audio descriptions across these use cases. The application has been downloaded over 500,000 times and is used in 70+ countries.
OrCam's MyEye platform runs comparable scene understanding models entirely on-device using a custom ARM-based processor, enabling functionality without any cloud connectivity. This architectural choice has made OrCam particularly popular in enterprise and institutional settings where data privacy requirements preclude cloud transmission of camera feeds.
Optical Character Recognition at Scale
OCR — the conversion of printed or displayed text to machine-readable format — is among the most mature computer vision applications in assistive technology. Current best-in-class systems achieve over 99% character-level accuracy on printed text in controlled conditions. The frontier challenge is real-world robustness: handwritten text, low-contrast signage, partially occluded labels, and non-Latin scripts remain areas of active research.
Google's ML Kit and Apple's Vision framework both provide on-device OCR APIs that assistive technology developers can integrate without building custom models. This commoditization of OCR has shifted competitive differentiation toward downstream capabilities: how quickly results are delivered to the user, how well the system handles multi-language documents, and how gracefully it degrades in poor lighting conditions.
Silicon Vendor Competitive Dynamics
The NPU performance race among silicon vendors has direct implications for assistive technology capability. Qualcomm, Apple, and MediaTek are the three vendors whose silicon appears most frequently in assistive devices and the smartphones that power them.
Qualcomm's strategy is explicitly developer-focused: the company's AI Hub platform provides pre-optimized model libraries for Hexagon NPU deployment, reducing the engineering effort required to port a computer vision model to on-device inference. For assistive technology startups with limited ML infrastructure teams, this matters.
Apple's approach leverages the vertical integration of hardware and software. The Core ML framework, optimized for Apple Neural Engine, enables iOS developers to deploy vision models with minimal configuration. Given that iPhone penetration among blind users is disproportionately high — driven by VoiceOver's long-standing reputation as the best mobile screen reader — Apple's ecosystem is a critical deployment target for any assistive vision application.
Barriers to Adoption and Research Gaps
Despite the technical progress, three barriers constrain market growth. First, dataset diversity: most computer vision models are trained on datasets that underrepresent the environments, lighting conditions, and object categories most relevant to blind users in low-income and non-Western contexts. Second, evaluation methodology: there is no standardized benchmark for assistive computer vision performance, making it difficult for buyers to compare competing systems. Third, regulatory clarity: the FDA and EU MDR have not yet issued definitive guidance on the classification of AI navigation devices, creating uncertainty for companies seeking reimbursement pathways.
Addressing these gaps will require coordinated investment from device manufacturers, academic research institutions, and standards bodies. The National Federation of the Blind and the Royal National Institute of Blind People have both called for the establishment of an independent assistive AI certification framework — a development that, if realized, would accelerate institutional adoption significantly.
Outlook
The $4.2 billion computer vision assistive technology market is not a distant projection — it is being built now, in the silicon roadmaps of Qualcomm and Apple, in the model architectures of research teams at MIT, Stanford, and ETH Zurich, and in the product pipelines of a growing cohort of well-funded startups. The companies that establish technical credibility and clinical evidence in 2026 will be positioned to capture a disproportionate share of that market as institutional procurement scales.