Postround

AI-Powered Mobile App for Automatic Golf Scorecard Recognition

Key results

    • 93 % marker-based detection accuracy after adaptive tuning
    • 90 % reduction in OCR errors through image-quality filtering
    • Cross-platform app with real-time table extraction and validation
AI-Powered Mobile App for Automatic Golf Scorecard Recognition
Services SERVICES
Mobile Development
Computer Vision
Industry INDUSTRY
Sports
Technologies TECHNOLOGIES USED
React Native
OCR
C++
Swift
Kotlin
Team TEAM
1 AI Engineer
1 React Native Developer
1 iOS Developer
1 Android Developer
Location LOCATION
United States
Duration PROJECT DURATION
4 months

“It-Jim helped us bring our golf analytics app to production, improving OCR accuracy and delivering core image processing modules. They adapted seamlessly to changing requirements and showed a strong commitment to quality. Their flexibility and technical expertise made them a reliable partner throughout the launch.”

Cameron Ward

Founder, Postround Golf

About the Client

About the Client

Postround Golf is a sports analytics startup focused on helping golfers understand their performance through data. Their platform turns traditional paper scorecards into structured digital insights. The solution analyzes each shot based on the lie, distance, and overall course strategy.

Postround is designed for golfers of all levels who want to access professional-level statistics for everyday rounds. By combining computer vision, OCR, and intuitive mobile interfaces, the app automates score tracking without requiring manual entry or external sensors.

The Challenges & Goals

Postround wanted to transform traditional golf scorecards into digital performance data. The task required solving several computer vision and ORC challenges under real-world conditions.

Accurate scorecard detection and alignment

Developing a robust CV pipeline to detect golf scorecards from handheld photos, align them geometrically, and isolate the data table despite variations in lighting, angle, and background.

Reliable text recognition and validation

Improving OCR accuracy for small, handwritten or printed text across diverse templates and handwriting styles, while ensuring correct mapping of values such as Lie, Par, and Score.

Handling variability in scorecard templates

Adapting to layout differences between scorecard designs by introducing marker-based identification and flexible geometrical transformation logic.

Solution Overview

It-Jim developed a mobile solution that combines computer vision, Optical Character Recognition (OCR), and backend integration to automate golf scorecard processing. The system detects scorecards in captured images, extracts the table area, validates its geometry, and prepares clean inputs for OCR. All core processing runs directly on the device through native Swift and Kolin modules integrated with custom computer vision. The final application works fully offline, ensuring stable performance and accurate results across different camera types, lighting conditions, and scorecard layouts.

Stage 1: Application Foundation

The first stage of cooperation with Postround focused on rebuilding a reliable foundation for the mobile application. Within the React Native framework, the team reimplemented the essential product logic and user flows, ensuring consistent performance and design across iOS and Android.

Key steps:

  • User authorization and profile management
  • Game session setup and tracking
  • Statistics and performance visualization
  • Local data storage and synchronization with the backend

The milestone delivered a stable cross-platform base, ready for the integration of AI-driven scorecard recognition and advanced analytics.

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Stage 2: AI Pipeline Research and Design

In parallel with mobile development, the AI team focused on defining the core scorecard processing architecture. We needed to determine how to detect, extract, and interpret data from golf scorecards reliably under real-world conditions.

Key deliverables:

  • Research and compared multiple approaches to blur, contrast, and marker detection
  • Designed a marker-based system for precise scorecard localization and ID assignment
  • Defined the geometry validation logic to ensure accurate table extraction
  • Established the technical stack and integration plan for offline operation on mobile devices

This stage resulted in a validated processing pipeline design and clear specifications for future on-device AI implementation.

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Stage 3: On-Device AI Integration

After the research phase, the team focused on embedding the designed computer vision pipeline into the mobile application. The objective was to enable real-time scorecard detection, table extraction, and text recognition directly on iOS and Android devices.

What we did to meet the requirements:

  • Integrated the C++ based CV core with native Swift and Kotlin modules
  • Implemented the ARUCO market detection and geometric transformation logic
  • Connected OCR and validation modules to the mobile workflow
  • Optimized performance to maintain accuracy and speed in offline mode

The team delivered a functional AI engine running entirely on the device, enabling golfers to capture and process scorecards without internet connectivity.

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Stage 4: Optimization & Launch

The final stage focused on improving recognition accuracy, refining OCR results, and preparing the product for public release. The team optimized the AI and application layers to ensure stability and reliable performance in real-world use.

Main challenges solved:

  • Tune ARUCO detection parameters to improve marker accuracy from early test results
  • Enhanced OCR reliability through two-stage image quality filtering and logic validation for Lies and Par fields
  • Conducted field testing on different devices and lighting conditions
  • Delivered production build for App Store and Google Play release

We delivered a production-ready mobile application that met all functional and performance requirements and helped golfers digitize and analyze their game data.

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Final Outcome

Postround now has a production-ready mobile app that turns paper scorecards into digital insights in seconds. The system runs fully offline, detects both printed and handwritten text, and automatically structures player data for performance analysis.

Key achievements:

  • Released to the App Store and Google Play
  • 93% marker-based detection accuracy after adaptive tuning
  • Up to 90% fewer OCR errors thanks to image-quality filtering and logic validation
  • Stable cross-platform MVP with integrated backend and analytics

The final product combines computer vision, OCR, and mobile engineering in a single, reliable solution that meets both technical and user expectations.

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