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Azure AI Vision · Computer Vision India

Extract Intelligence from
Images, Video & Documents

Azure AI Vision delivers state-of-the-art computer vision — image analysis, OCR, custom object detection, video indexing, and spatial analytics — via simple REST APIs. SchwettmannTech implements Vision AI for Indian manufacturers (defect detection), retailers (shelf analytics), logistics providers (document scanning), BFSI firms (KYC automation), and healthcare providers (medical image analysis).

Azure AI Vision Certified Partner India
50+ Vision Solutions Deployed
Aadhaar & PAN KYC Extraction
99%
OCR accuracy on printed & handwritten text
Real-time
Video stream analysis for smart factory
Custom
Defect detection with 50+ images
KYC
Aadhaar, PAN & passport extraction
Azure AI Vision · Operations Dashboard
Live
14,200
Docs/Day Scanned
↑ OCR Auto
99.1%
Field Accuracy
↑ Extraction
3ms
Vision API
↓ Latency
0
Manual QC
↓ 100%
Image Analysis 4.0 — Product Catalogue
4,200 SKUs · Florence model · Auto caption & tag
Live
OCR — Invoice & ID Extraction Pipeline
8,000 invoices/day · Hindi+English · Dataverse push
Running
Custom Vision — PCB Defect Detection
Real-time · 98.4% accuracy · Azure IoT Edge
Active
KYC Automation — Aadhaar+PAN Extraction
RBI digital KYC · Liveness check · Auto D365 push
Compliant
Vision AI Insight: Custom Vision defect model detected 47 PCB anomalies in last 8 hours — 99.2% precision, 0 false positives on today's production run. Quality reject rate this week: 0.3% vs 2.1% manual inspection baseline.
99%
OCR Accuracy on Printed & Handwritten Text
Real-time
Video Stream Analysis for Smart Factory & Retail
Custom
Train Defect Models with as Few as 50 Images
KYC
Aadhaar, PAN & Passport Extraction India
Image Analysis 4.0Florence Foundation ModelOCR DevanagariTamil Script OCRCustom Vision DefectsVideo IndexerSpatial AnalysisFace APIAzure IoT Edge VisionKYC AutomationLiveness DetectionD365 IntegrationImage Analysis 4.0Florence Foundation ModelOCR DevanagariTamil Script OCRCustom Vision DefectsVideo IndexerSpatial AnalysisFace APIAzure IoT Edge VisionKYC AutomationLiveness DetectionD365 Integration
Services

Azure AI Vision Service Capabilities

Our certified Azure AI engineers implement the full Vision AI portfolio — from real-time defect detection on factory floors to automated KYC for BFSI digital onboarding.

Image Analysis 4.0
Detect objects, scenes, activities, and brands in images using the Florence foundation model. Generate rich captions automatically, extract dominant colours, detect adult content, and read text — all via a single API call. Analyse product images at scale, automate content moderation, and power visual search for e-commerce catalogues.
Florence Model · Object Detection · Captioning · Content Moderation
OCR & Multilingual Document Scanning
Extract printed and handwritten text from invoices, contracts, ID documents, and forms with 99%+ accuracy. Supports Devanagari (Hindi), Tamil, Telugu, Kannada, Bengali, and Gujarati scripts alongside English — essential for Indian multilingual document processing, GST invoice automation, and healthcare record digitisation.
OCR · Hindi Devanagari · Tamil · Telugu · GST Invoices · Multi-language
Custom Vision — Defect Detection
Train custom image classification and object detection models with 50+ labelled images using Azure Custom Vision. Manufacturing use cases: surface defect detection on production lines, PCB inspection, packaging quality control, and raw material grading — deployable in real-time on Azure IoT Edge for zero-latency factory floor decisions.
Custom Vision · Defect Detection · IoT Edge · Manufacturing QC
Video Indexer & Content Intelligence
Automatically transcribe, translate, and index video content. Extract faces, brands, topics, emotions, keywords, and scenes. EdTech companies index lecture libraries for searchability; media firms tag large archives; manufacturing firms analyse safety training videos for compliance verification.
Video Indexer · Transcription · Topic Extraction · Scene Detection
Spatial Analysis & Footfall
Real-time people counting, zone entry/exit monitoring, queue length measurement, and crowd density analysis from CCTV streams. Retail footfall and dwell time analytics, smart building occupancy for energy optimisation, and factory floor safety monitoring — all without storing or transmitting video footage.
Spatial Analysis · Footfall Analytics · Queue Monitoring · Smart Building
KYC & Identity Document Extraction
Automated extraction of Aadhaar card, PAN card, passport, driving licence, and Voter ID data for BFSI digital onboarding. Liveness detection for selfie verification. RBI-compliant video KYC integration — push extracted data directly to Dynamics 365 Customer Service or Dataverse for straight-through processing.
KYC · Aadhaar · PAN · Liveness Detection · RBI Video KYC · D365
Capabilities

Complete Capability Coverage

Our certified team covers every facet of this service — from strategy and implementation to managed operations and continuous optimisation.

Manufacturing

Factory Vision AI on Azure IoT Edge

Deploy Custom Vision defect detection models on Azure IoT Edge devices directly on the production line — zero cloud latency, real-time decisions at the machine, offline resilience. Detected defects trigger automated reject mechanisms and quality alerts.

  • Azure IoT Edge Deployment
  • Real-time <10ms Inference
  • Offline Resilience
  • Automated Reject Trigger
📄Documents

Multilingual Indian Document OCR

Azure Read API handles the full range of Indian language scripts — Devanagari, Tamil, Telugu, Kannada, Gujarati, Bengali — with layout analysis that preserves table structure, paragraph boundaries, and form field relationships for accurate data extraction.

  • Hindi Devanagari OCR
  • Tamil & Telugu Script
  • Table Structure Preservation
  • Form Field Extraction
BFSI

KYC Automation India

End-to-end digital KYC: document extraction (Aadhaar, PAN), face match to document photo, liveness detection, and direct push to D365 CRM — reducing customer onboarding from 3 days to 15 minutes while maintaining RBI compliance.

Retail

Shelf & Footfall Analytics

Real-time shelf occupancy monitoring detects out-of-stock situations within minutes. Spatial analysis tracks footfall heat maps, queue lengths, and dwell times — all without storing video footage, complying with India's privacy requirements.

🎥Video

Video Intelligence at Scale

Azure Video Indexer processes hours of content in minutes — transcribing, tagging, and making video searchable. Training video libraries, recorded meetings, security footage, and broadcast archives all become structured, queryable data assets.

🔒Privacy

Privacy-First Vision Design

Azure AI Vision processes images and video frames within your Azure tenant — video streams are analysed in real-time with only metadata (counts, coordinates, events) stored. No raw biometric data stored without consent. DPDP Act 2023 and India's IT Rules 2021 compliant by design.

Performance

Vision at Millisecond Scale

Image Analysis 4.0 API responds in 3–15ms, enabling real-time production line inspection at 60+ frames per second on Azure IoT Edge. Cloud Vision API handles 1,000+ https://schwettmanntech.com/wp-content/themes/schwettmanntech_wp/assets/images/second with auto-scaling — no capacity planning required.

Delivery

Our Azure AI Vision Delivery Framework

A structured 3–5 week delivery process — from requirements and data labelling to production deployment and monitoring.

1
Phase 1 — Week 1
Requirements & Data Collection
Define use case and collect training images
Document the vision AI use case — defect types, document formats, or spatial zones to monitor. Collect and label training images using Azure Custom Vision's labelling tool or AI-assisted labelling. Minimum 50 images per class recommended; 200+ for production accuracy.
Use Case SpecImage CollectionData Labelling
2
Phase 2 — Week 1–2
Model Training & API Configuration
Train custom models and configure services
Train Custom Vision model iteratively — evaluating precision/recall on validation set. Configure Image Analysis 4.0 for pre-built capabilities. Set up OCR pipeline with post-processing logic for field extraction and validation rules.
Custom Vision TrainingModel EvaluationOCR Pipeline
3
Phase 3 — Week 2–3
Integration & IoT Edge (if applicable)
Connect to business systems and deploy edge
Integrate Vision APIs with Dynamics 365, Power Apps, or custom portals. For manufacturing deployments: package model as Docker container, deploy to Azure IoT Edge on factory floor devices. Set up alert routing to Teams or email.
API IntegrationIoT Edge DeployAlert Routing
4
Phase 4 — Week 3–5
UAT, Go-Live & Monitoring
Test in production conditions and go live
UAT with real production images under actual lighting and conditions. Performance testing at production throughput. Go-live with Azure Monitor dashboards tracking accuracy, throughput, and anomalies. Monthly model refresh cycle for drift management.
UATPerformance TestGo-LiveMonitor Dashboard
Industries

Azure AI Vision for Indian Industries

Computer vision deployed for India-specific use cases — from multilingual OCR to manufacturing defect detection and BFSI KYC compliance.

Manufacturing · QC AI
BFSI · KYC Auto
Retail · Shelf AI
Healthcare · Imaging
Logistics · Barcode
EdTech · Engagement
Construction · Safety
Energy · Meter Read
India-Specific Vision AI Capabilities

SchwettmannTech has trained Custom Vision models on Indian manufacturing defect datasets, Indian language document layouts (GST invoices, Aadhaar cards, PAN cards), and Indian retail shelf planogram formats. Our OCR configurations handle the Unicode complexity of Devanagari and Dravidian scripts with layout preservation. KYC automation meets RBI Master Direction on Digital KYC 2021 and UIDAI Aadhaar authentication framework requirements.

Business Impact

Proven Results: Azure AI Vision Results

Outcomes from SchwettmannTech's Azure AI Vision deployments across Indian enterprises.

99%
OCR accuracy on Indian language documents — invoices, ID cards, and forms
98%
Custom Vision defect detection precision on manufacturing production lines
15 min
BFSI customer onboarding time after KYC automation vs 3 days manual
70%
Reduction in quality inspection labour costs after Vision AI deployment
Customer Stories

What Our Clients Say

"SchwettmannTech deployed Azure Custom Vision for PCB defect detection on our SMT line. The model detects solder bridges, missing components, and tombstoning at 99.1% accuracy running on Azure IoT Edge — decisions in under 10ms without cloud round-trip. Defect escape rate dropped from 0.8% to 0.04% in three months. The ROI paid for itself in prevented warranty claims within 6 months."

VK
Vivek Krishnan
Head of Quality · Electronics Manufacturer, Chennai

"We process 12,000 KYC applications a day across our NBFC branches. Azure AI Vision's Aadhaar and PAN extraction — combined with liveness detection — reduced our KYC processing time from 3 days to 18 minutes per customer. RBI compliance is built in: all processing happens within our Azure tenant, audit logs are preserved, and no biometric data is stored beyond the verification session."

MS
Meera Sharma
Head of Digital Banking · NBFC, Mumbai

"Our 8-store retail chain deployed Azure Spatial Analysis for footfall analytics and shelf monitoring. Within 30 days we identified 3 store layouts causing customer flow bottlenecks, and out-of-stock alerts now fire within 8 minutes of a gap appearing on shelf. Revenue per square foot improved 14% across monitored stores in the first quarter."

AJ
Anil Joshi
COO · Retail Chain, Ahmedabad
FAQs

Common Azure AI Vision Questions

Have a specific computer vision use case? Our Azure AI engineers can assess feasibility and data requirements in a free 30-minute scoping call.

Talk to a Vision AI Engineer
Azure Custom Vision can train an initial model with as few as 15 images per class (object detection requires 15 labelled examples per tag), but production accuracy typically requires 50–100 labelled images per class for classification and 100–200 per tag for object detection. For manufacturing defect detection, we recommend collecting defect samples across different lighting conditions, camera angles, and product batches that represent real production variation. SchwettmannTech provides AI-assisted labelling tools to accelerate data preparation — we've labelled datasets of 1,000+ images in 2–3 days.
Azure AI Vision's Read API handles printed text in Devanagari (Hindi), Tamil, Telugu, Kannada, Gujarati, and Bengali with high accuracy. Handwritten text in Indian scripts has lower accuracy than printed — handwritten Devanagari accuracy is approximately 85–90% depending on writing style, which is sufficient for many forms and applications but requires human review for high-stakes documents. For the highest-stakes handwritten Indian language OCR, we supplement Azure with custom post-processing. Printed Hindi and other Indian language scripts achieve 95–98% character accuracy, suitable for most invoice and document processing applications.
No — Azure Spatial Analysis processes live video streams in real-time and outputs only anonymised metadata: people counts, zone entry/exit events, queue lengths, and dwell times. Raw video frames are never stored or transmitted to Microsoft. The Face API can detect and verify faces but SchwettmannTech configures all Spatial Analysis deployments without persistent face identification to comply with India's DPDP Act 2023 and IT Rules 2021 requirements for biometric data protection. Physical access control applications using face recognition require explicit user consent and are designed with data minimisation principles.
We have three standard integration patterns: (1) Power Automate flow — triggered when a document arrives in SharePoint or email, calls Vision OCR API, extracts fields, and creates/updates Dynamics 365 records. This requires no custom code and deploys in 1–3 days. (2) Azure Function middleware — for high-volume (1,000+ docs/day) processing, an Azure Function processes documents asynchronously and pushes to Dataverse via the Web API. (3) D365 Plugin — for server-side processing within D365 workflows, a plugin calls Azure Functions that invoke Vision APIs. The right pattern depends on document volume, latency requirements, and existing D365 architecture.
Yes — Azure Custom Vision models can be exported as Docker containers (ONNX, CoreML, TensorFlow) and deployed on Azure IoT Edge devices on your factory floor. This is essential for manufacturing environments where network connectivity may be unreliable or where you need sub-10ms inference latency for real-time line control. SchwettmannTech packages Custom Vision models as IoT Edge modules, deploys them via Azure IoT Hub, and manages model updates over-the-air — updating the edge model without manual device visits.

Deploy Vision AI on Your Factory Floor or BFSI Platform

Book a free Azure AI Vision Feasibility Assessment. We'll evaluate your image data, demonstrate a working prototype on your samples, and deliver an implementation roadmap — no commitment required.

Azure AI Services Azure Machine Learning