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How We Enabled Toters Menu Image Recognition Using ML & OCR to Improve Food Ordering Accuracy

How we enabled Toters Menu Image Recognition using ML & OCR to automate menu analytics, improve accuracy, and streamline food ordering processes.

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Navratri Mega Sale Price Tracking

Quick Overview

Our engagement with Toters focused on implementing Toters Menu Image Recognition using ML & OCR to enhance menu accuracy, streamline order processing, and improve customer satisfaction. The project spanned four months and aimed to automate menu data extraction from images across multiple restaurants. By leveraging machine learning and optical character recognition, we enabled accurate identification of menu items, prices, and categories. Key impact metrics included:

  • 98%+ accuracy in menu data extraction
  • 5× faster menu updates
  • Real-time integration of new menu items across the platform

This solution allowed Toters to maintain a consistent, up-to-date menu across its e-commerce platform, enhancing operational efficiency and user experience.

The Client

Navratri Mega Sale Price Tracking

Toters is a leading food delivery platform in the Middle East, connecting restaurants with consumers via its mobile and web platforms. In an increasingly competitive food delivery industry, accurate menu representation is essential to retain customers and reduce order errors. The rise of digital ordering and changing consumer preferences has created pressure for real-time menu updates.

Before partnering with Actowiz Solutions, Toters faced operational inefficiencies in updating menus. Manual entry of menu items, prices, and categories led to inconsistencies, delayed updates, and occasional inaccuracies. Restaurants frequently updated menus with new dishes, promotions, and pricing, but the lack of automation made it challenging to keep the platform synchronized.

Through Menu Image Data Extract for Toters, our team implemented a solution to automatically capture menu information from restaurant images. This approach eliminated manual errors, reduced the time required for updates, and ensured that customers had access to accurate menu information in real time. It set the foundation for smarter analytics, faster operational workflows, and improved customer satisfaction across the Toters platform.

Goals & Objectives

Goals

The business goal was to enhance order accuracy, streamline menu updates, and scale menu management efficiently. By implementing Menu image processing for Toters using AI, the client aimed to reduce operational bottlenecks and improve customer experience.

Objectives
  • Automate extraction of menu items, prices, and categories from images
  • Integrate data into Toters’ backend systems for real-time updates
  • Standardize menu structure across multiple restaurants
  • Enable analytics on menu trends and popular dishes
KPIs
  • Menu extraction accuracy: 98%+
  • Time to update new menu items: reduced from 3 days to 6 hours
  • Number of restaurants integrated per week: 50+
  • Reduction in customer complaints due to incorrect orders: 85%

Our approach ensured a measurable improvement in speed, accuracy, and operational efficiency, aligning technical objectives with Toters’ business goals.

The Core Challenge

Prior to our solution, Toters struggled with several operational challenges. Manual menu updates caused OCR-powered menu Data extraction for Toters to be slow and error-prone. Restaurants submitted menus in various formats—images, PDFs, or scanned files—making standardization difficult.

High variability in fonts, languages, and menu layouts led to inconsistent data extraction. Errors in prices, dish names, or categories directly impacted customer satisfaction and generated complaints. Frequent menu updates meant manual processes could not keep pace with the speed of the food delivery market.

Additionally, there was no centralized system for tracking menu changes or performing analytics on menu performance. Toters needed a solution that could extract structured data automatically, normalize it, and integrate it into their platform efficiently.

The lack of automation and inconsistent data impacted operational speed, order accuracy, and analytics capabilities. Our goal was to resolve these pain points with a robust, AI-driven solution that ensured reliable OCR-powered menu Data extraction for Toters, enabling real-time updates and accurate menu representation across all restaurants.

Our Solution

We implemented a ML-based menu structure recognition solution in multiple phases to address Toters’ challenges.

Phase 1 – Requirement Analysis & Data Collection:

We analyzed restaurant menus to understand variability in layout, fonts, and languages. This phase helped define the scope of Toters Menu Image Recognition using ML & OCR.

Phase 2 – ML Model Development:

Custom machine learning models were trained to recognize text, dish categories, prices, and special instructions from menu images. OCR was enhanced with deep learning techniques to handle diverse fonts and layouts.

Phase 3 – Data Normalization:

Extracted data was structured into a standardized format for integration into Toters’ backend. Dish names, prices, and categories were cleaned and normalized to ensure consistency across restaurants.

Phase 4 – Real-Time Integration:

Automated pipelines pushed processed data into Toters’ platform, enabling real-time menu updates. Alerts were configured for new dishes, promotions, and price changes.

Phase 5 – Analytics & Reporting:

The extracted data powered analytics dashboards, highlighting popular dishes, trending categories, and menu performance metrics.

Phase 6 – Continuous Improvement:

Models were continuously retrained using new menu images, improving accuracy over time. Feedback loops ensured that anomalies were quickly corrected.

By implementing ML-based menu structure recognition, we enabled Toters to reduce manual effort, maintain accurate menus, and enhance operational speed, delivering measurable improvements in order accuracy and customer satisfaction.

Results & Key Metrics

Key Performance Metrics
  • Menu extraction accuracy: 98.7%
  • Average time to update menus: reduced from 72 hours to 6 hours
  • Number of restaurants automated per week: 50+
  • Reduction in order errors: 85%
  • Real-time menu updates delivered for: 1,000+ dishes
Results Narrative

The implementation allowed Toters to Extract Toters Food Delivery Data efficiently from images, PDFs, and scanned menus. Real-time integration ensured that customers always saw accurate menus, reducing complaints and increasing satisfaction. Analytics on dish popularity and pricing trends provided actionable insights for restaurants and the platform. The automated process scaled seamlessly across hundreds of restaurants, enabling rapid onboarding and continuous menu updates. Overall, Toters achieved faster operational workflows, improved accuracy, and better data-driven decision-making, enhancing its competitive edge in the food delivery market.

What Made Product Data Scrape Different?

Our solution leveraged Scrape Restaurant Menu Data, Toters Menu Image Recognition using ML & OCR with proprietary machine learning frameworks and automated pipelines. Unlike traditional manual processes, our approach handled thousands of menu images daily, normalized diverse layouts, and integrated data into backend systems in real time. Smart automation reduced human intervention, ensured accuracy, and scaled easily across hundreds of restaurants. The combination of ML-based recognition, OCR enhancements, and continuous retraining made the solution innovative, enabling Toters to maintain accurate menus, improve order accuracy, and gain actionable insights for data-driven operational and strategic decisions.

Client Feedback

"Working with Actowiz Solutions on Toters Menu Image Recognition using ML & OCR has transformed how we manage menus. The automated system extracts menu items, prices, and categories accurately, saving us hours of manual work each week. Our platform now updates menus in real time, reducing errors and improving customer satisfaction. The analytics dashboards provide insights into popular dishes and trends, helping us make informed decisions. The team’s expertise in AI, OCR, and automation was evident throughout the project. This solution has given Toters a significant operational and competitive advantage in the food delivery market."

— Head of Technology, Toters

Conclusion

Implementing Web scraping API, Custom Datasets, and instant data scraper technologies enabled Toters to automate menu data extraction, improve accuracy, and streamline operations. By leveraging ML and OCR, the platform now provides real-time updates, reducing errors and enhancing customer experience. Restaurants benefit from accurate representation of menu items, prices, and categories, while Toters gains actionable analytics on trends and dish popularity. This project demonstrates the power of AI-driven data solutions in the food delivery sector. Actowiz Solutions continues to support Toters’ innovation journey, ensuring scalable, accurate, and efficient menu management across the platform.

FAQs

Q1: How does the menu image recognition work?

The system uses ML and OCR to extract text, prices, and categories from restaurant menu images, PDFs, or scans, then normalizes the data for integration.

Q2: Can it handle multiple languages and fonts?

Yes, models are trained on diverse layouts, languages, and font styles to ensure high accuracy across restaurants.

Q3: How fast is menu updating?

Menus are updated in real time, reducing previous delays from 72 hours to under 6 hours.

Q4: Is manual intervention required?

Minimal intervention is needed; the automated pipeline handles extraction, normalization, and integration efficiently.

Q5: Can this be extended to other food delivery platforms?

Yes, the framework is scalable and can integrate other restaurant platforms, enabling wider Toters Menu Image Recognition using ML & OCR coverage.

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EV Charging Stations vs Gas Stations Analysis in the US - Geographic Coverage, Policy Impact, and Competitive Landscape Insights

In-depth analysis comparing EV charging stations and gas stations in the US, covering coverage, policies, competition, and future growth trends.
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