OMR based Live, Detection-Based Camera App Development

@mrresearcher99

Hello, I’d be happy to help you implement this live camera detection and auto-capture feature. I have 5+ years of experience in Android, iOS, and React Native, including camera-based applications, real-time processing, and document scanning workflows. I have previously worked on features such as QR/document scanning, edge detection, auto-capture logic, and camera overlays, so I’m comfortable building a solution that is fast, stable, and accurate on both platforms. Approach (high level): Use Expo Camera for live preview and high-resolution capture Integrate a real-time CV layer (OpenCV / native module or optimized JS-based detection) to identify black square contours Validate presence of 4 corner boxes, check alignment and stability over a few frames Dynamically update overlay (neutral → green) Trigger automatic capture only when all validation rules pass Build a clean preview screen with Save and Retake actions and proper state reset What you’ll get: Cross-platform working module (Android & iOS) Real-time detection with green overlay confirmation Auto high-res capture Preview + Save/Retake flow Clean, documented code and short demo video I focus on performance, accuracy, and maintainable code. I’m confident I can deliver a reliable scanning experience suitable for OMR/document-style use cases. Let’s discuss your timeline and any sample reference images or box size constraints if available. Best, Bhargav

@ZohaibRoy

⭐⭐⭐⭐⭐ Create Live Camera Detection for Four Black Boxes in React Native ❇️ Hi My Friend, I hope you are doing well. I reviewed your project requirements and see you are looking for a React Native developer for live camera detection. Look no further; Zohaib is here to help you! My team has completed over 50 similar projects for camera-based applications. I will implement real-time detection using Expo Camera and optimize the app for both Android and iOS. ➡️ Why Me? I can easily do your live camera detection project as I have 5 years of experience in React Native development, specializing in camera functionality, computer vision, and image processing. I have a strong grip on OpenCV and various optimization techniques to ensure smooth performance. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ React Native ✅ Expo Camera ✅ OpenCV ✅ Computer Vision ✅ Image Processing ✅ JavaScript ✅ UI/UX Design ✅ Real-time Detection ✅ Mobile App Development ✅ Cross-Platform Development ✅ Performance Optimization ✅ API Integration Waiting for your response! Best Regards, Zohaib

@Irshad0592

Hello Mr. Rajeev Kumar, I’ve delivered similar camera + OCR/verification systems for background checking software that require real-time accuracy. I can help you to build live camera detection of 4 black corner boxes with auto capture using React Native (Expo) for iOS & Android. Approach: real-time detection → validate 4 corners & alignment → green overlay → auto high-res capture → preview with Save / Retake. Clean, documented code + demo video included. Happy to discuss lighting and edge cases before starting. Best, Mohammad

@kaustubhbawa123

Hi, I am an IIT Grad, MongoDB Certified Developer, ex-BFSI and worked at fortune 500 companies. I will make it a reality for you. As a Full Stack Developer, I will deliver a high-quality solution using modern technologies and industry best practices. Kindly click on the chat button so we can discuss and get started. Will share you my prior projects done and my resume too. I have been doing freelancing since 2019 worked at top MNCs in both USA and India. Lets connect

@Mayanks010

Hi there, You’re absolutely in the RIGHT PLACE. I’ve delivered SIMILAR PROJECTS multiple times and know EXACTLY how to execute this efficiently and correctly from day one. To lock down the SCOPE, TIMELINE, AND PRICING, I’ll need to ask you a few key questions. Unfortunately, Freelancer’s 1500 CHARACTER LIMIT doesn’t allow me to break everything down properly here. Let’s jump on CHAT so I can show you my PROVEN PAST WORK, walk you through the REAL RESULTS I’ve delivered, and outline a CLEAR ACTION PLAN for your project. You’ll immediately see why my approach is DIFFERENT and EFFECTIVE. If you’re serious about getting this done RIGHT, I’m ready to move forward. Looking forward to CONNECTING and WINNING TOGETHER. Cheers, Mayank Sahu

@jonhw5

Hello, thanks for posting this project. I've reviewed your requirements for the live camera detection, auto-capture, and preview flow, and I believe this aligns very well with my development expertise. I have solid experience creating robust camera and image processing features in React Native (Expo) and have integrated computer vision approaches such as OpenCV for real-time detection tasks. My technical approach will ensure reliable detection of the four black boxes with efficient overlay management, smooth auto-capture logic, and seamless Save/Retake workflows. I prioritize cross-platform performance (Android & iOS), clean code, and user-friendly preview flows to meet your high reliability and UX standards. Could you please specify which devices this needs to be demoed on for your validation process, or should the demo video cover both platforms? Looking forward to hearing from you. Warm regards, Vitalii.

@AlexDeveloper911

Hi, We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on Mobile App Development, iPhone, Android, C++ Programming, Image Processing, OpenCV, Documentation, React Native, Computer Vision, Expo Please come over chat and discuss your requirement in a detailed way. Thank You

@vlaso

Hello, I’m very interested in implementing your live camera detection module for React Native (Expo). I have experience building real-time camera scanning apps with computer vision features and can deliver a performant, cross-platform solution for iOS and Android. Approach: Use Expo Camera for live preview and high-resolution capture. Implement real-time detection of four black boxes using OpenCV (via WebAssembly or Expo-compatible wrapper) or optimized CV algorithms. Validate detection to ensure all corners are present, properly aligned, and stable. Change overlay color dynamically: neutral while detection is incomplete, green when all corners are detected, triggering automatic capture. Provide preview screen post-capture with Save and Retake options, ensuring detection resets correctly on retake. Deliverables: Fully functional live camera module with detection, overlay, and auto-capture. Preview, Save, and Retake flow. Clean, well-documented source code. Demo video showcasing live detection, auto-capture, and save/retake workflow. I have prior experience with document scanning and OMR-style detection, and I’ll ensure real-time performance and cross-platform reliability. Estimated timeline: 2–3 weeks for a fully functional MVP, depending on testing and device calibration. Looking forward to building this feature and delivering a smooth, accurate camera scanning experience.

@appsturn5520

Hello, I can implement the live camera detection of four black corner boxes with automatic capture using React Native (Expo) for both Android and iOS. I clearly understand this is a document / OMR-style scanning feature where alignment, stability, and real-time accuracy are critical. Approach Use Expo Camera for live preview Implement real-time black square corner detection using an Expo-compatible computer vision approach (OpenCV/WASM or optimized CV logic) Validate: All 4 black boxes detected Correct square/rectangle geometry Proper alignment and camera stability Overlay & Capture Neutral overlay when detection is incomplete Green overlay only when all conditions are met Auto-capture high-resolution image once overlay turns green Prevent capture if corners are missing, misaligned, or camera is moving Preview Flow Show captured image preview Save (confirm & store image) Retake (reset detection and return to live camera) Deliverables Fully working camera module Live detection + green confirmation overlay Auto capture + preview screen Clean, documented code Short demo video Timeline: ~5–7 days Budget: Fits within $30–$250 USD I can start immediately and fine-tune detection accuracy based on feedback. Best regards, Appstrn software Solution

@photop2

I will implement a high-performance React Native (Expo) camera module that detects four black corner boxes in real time, validates alignment and stability, triggers a green overlay, and automatically captures a high-resolution image with preview, save, and retake flow. Work will be performed Monday–Friday, 8 hours/day (CET) with immediate start, using Expo Camera and an optimized computer-vision approach compatible with iOS and Android. Core detection logic, overlay states, and auto-capture will be completed within 10–14 calendar days, followed by 3–4 days for preview flow, optimization, and cross-device testing. Final delivery includes clean, well-documented source code and a short demo video demonstrating live detection, auto capture, and save/retake functionality.

@osamak74

Dear Client, Thank you for sharing your exciting project on live camera detection for OMR-style scanning. I fully understand your goal of creating an app that accurately detects four black boxes using React Native (Expo), enabling seamless image capture and user interaction. With over three years of experience in mobile app development, I have successfully built similar camera applications that incorporate real-time detection using OpenCV. In my last project, I developed a live detection feature that utilized a camera overlay system, resulting in a 95% accuracy rate in object detection. ✅ My Plan: - Implement Expo Camera for live preview. - Utilize OpenCV for accurate detection of black boxes. - Create a responsive camera overlay with clear visual feedback. - Design automation for image capture following successful detection. - Integrate features for previewing, saving, and retaking images with intuitive UX. To ensure alignment with your vision, could you clarify any specific design preferences for the overlay? Best regards, Osama Khan