Evolphin Software, Inc. is a leading provider of digital and media asset management solutions based in Silicon Valley, California. Crop.photo from Evolphin Software is a cloud-based service that offers powerful bulk processing tools for automating image cropping, content resizing, background removal, and listing image analysis.
Crop.photo is tailored for high-end retailers, ecommerce platforms, and sports organizations. The solution has created a unique offering for bulk image editing through its advanced AI-driven solutions. In this post, we explore how Crop.photo uses Amazon Rekognition to provide sophisticated image analysis, enabling automated and precise editing of large volumes of images. This integration streamlines the image editing process for clients, providing speed and accuracy, which is crucial in the fast-paced environments of ecommerce and sports.
Automation: The way out of bulk image editing challenges
Bulk image editing isn’t just about handling a high volume of images, it’s about delivering flawless results with speed at scale. Large retail brands, marketplaces, and sports industries process thousands of images weekly. Each image must be catalog-ready or broadcast-worthy in minutes, not hours.
The challenge lies not just in the quantity but in maintaining high-quality images and brand integrity. Speed and accuracy are non-negotiable. Retailers and sports organizations expect rapid turnaround without compromising image integrity.
This is where Crop.photo’s smart automations come in with an innovative solution for high-volume image processing needs. The platform’s advanced AI algorithms can automatically detect subjects of interest, crop the images, and optimize thousands of images simultaneously while providing consistent quality and brand compliance. By automating repetitive editing tasks, Crop.photo enables enterprises to reduce image processing time from hours to minutes, allowing creative teams to focus on higher-value activities.
Challenges in the ecommerce industry
The ecommerce industry often encounters the following challenges:
- Inefficiencies and delays in manual image editing – Ecommerce companies rely on manual editing for tasks like resizing, alignment, and background removal. This process can be time-consuming and prone to delays and inconsistencies. A more efficient solution is needed to streamline the editing process, especially during platform migrations or large updates. Maintaining uniformity across diverse image types – Companies work with a variety of image types, from lifestyle shots to product close-ups, across different categories. Maintaining uniformity and professionalism in all image types is essential to meet the diverse needs of marketing, product cataloging, and overall brand presentation. Large-scale migration and platform transition – Transitioning to a new ecommerce platform involves migrating thousands of images, which presents significant logistical challenges. Providing consistency and quality across a diverse range of images during such a large-scale migration is crucial for maintaining brand standards and a seamless user experience.
For a US top retailer, wholesale distribution channels posed a unique challenge. Thousands of fashion images need to be made for the marketplace with less than a day’s notice for flash sales. Their director of creative operations said,
“Crop.photo is an essential part of our ecommerce fashion marketplace workflow. With over 3,000 on-model product images to bulk crop each month, we rely on Crop.photo to enable our wholesale team to quickly publish new products on popular online marketplaces such as Macy’s, Nordstrom, and Bloomingdales. By increasing our retouching team’s productivity by over 70%, Crop.photo has been a game changer for us. Bulk crop images used to take days can now be done in a matter of seconds!”
Challenges in the sports industry
The sports industry often contends with the following challenges:
- Bulk player headshot volume and consistency – Sports organizations face the challenge of bulk cropping and resizing hundreds of player headshots for numerous teams, frequently on short notice. Maintaining consistency and quality across a large volume of images can be difficult without AI. Diverse player facial features – Players have varying facial features, such as different hair lengths, forehead sizes, and face dimensions. Adapting cropping processes to accommodate these differences traditionally requires manual adjustments for each image, which leads to inconsistencies and significant time investment. Editorial time constraints – Tight editorial schedules and resource limitations are common in sports organizations. The time-consuming nature of manual cropping tasks strains editorial teams, particularly during high-volume periods like tournaments, where delays and rushed work can impact quality and timing.
An Imaging Manager at Europe’s Premier Football Organization expressed,
“We recently found ourselves with 40 images from a top flight English premier league club needing to be edited just 2 hours before kick-off. Using the Bulk AI headshot cropping for sports feature from Crop.photo, we had perfectly cropped headshots of the squad in just 5 minutes, making them ready for publishing in our website CMS just in time. We would never have met this deadline using manual processes. This level of speed was unthinkable before, and it’s why we’re actively recommending Crop.photo to other sports leagues.”
Solution overview
Crop.photo uses Amazon Rekognition to power a robust solution for bulk image editing. Amazon Rekognition offers features like object and scene detection, facial analysis, and image labeling, which they use to generate markers that drive a fully automated image editing workflow.
The following diagram presents a high-level architectural data flow highlighting several of the AWS services used in building the solution.
The solution consists of the following key components:
- User authentication – Amazon Cognito is used for user authentication and user management. Infrastructure deployment – Frontend and backend servers are used on Amazon Elastic Container Service (Amazon ECS) for container deployment, orchestration, and scaling. Content delivery and caching – Amazon CloudFront is used to cache content, improving performance and routing traffic efficiently. File uploads – Amazon Simple Storage Service (Amazon S3) enables transfer acceleration for fast, direct uploads to Amazon S3. Media and job storage – Information about uploaded files and job execution is stored in Amazon Aurora. Image processing – AWS Batch processes thousands of images in bulk. Job management – Amazon Simple Queue Service (Amazon SQS) manages and queues jobs for processing, making sure they’re run in the correct order by AWS Batch. Media analysis – Amazon Rekognition services analyze media files, including:
- Face Analysis to generate headless crops. Moderation to detect and flag profanity and explicit content. Label Detection to provide context for image processing and focus on relevant objects. Custom Labels to identify and verify brand logos and adhere to brand guidelines.
Amazon Rekognition is an AWS computer vision service that powers Crop.photo’s automated image analysis. It enables object detection, facial recognition, and content moderation capabilities:
- Face detection – The Amazon Rekognition face detection feature automatically identifies and analyzes faces in product images. You can use this feature for face-based cropping and optimization through adjustable bounding boxes in the interface. Image color analysis – The color analysis feature examines image composition, identifying dominant colors and balance. This integrates with Crop.photo’s brand guidelines checker to provide consistency across product images. Object detection – Object detection automatically identifies key elements in images, enabling smart cropping suggestions. The interface highlights detected objects, allowing you to prioritize specific elements during cropping. Custom label detection – Custom label detection recognizes brand-specific items and assets. Companies can train models for their unique needs, automatically applying brand-specific cropping rules to maintain consistency. Text detection (OCR) – The OCR capabilities of Amazon Recognition detect and preserve text within images during editing. The system highlights text areas to make sure critical product information remains legible after cropping.
Within the Crop.photo interface, users can upload videos through the standard interface, and the speech-to-text functionality will automatically transcribe any audio content. This transcribed text can then be used to enrich the metadata and descriptions associated with the product images or videos, improving searchability and accessibility for customers. Additionally, the brand guidelines check feature can be applied to the transcribed text, making sure that the written content aligns with the company’s branding and communication style.
The Crop.photo service follows a transparent pricing model that combines unlimited automations with a flexible image credit system. Users have unrestricted access to create and run as many automation workflows as needed, without any additional charges. The service includes a range of features at no extra cost, such as basic image operations, storage, and behind-the-scenes processing.
For advanced AI-powered image processing tasks, like smart cropping or background removal, users consume image credits. The number of credits required for each operation is clearly specified, allowing users to understand the costs upfront. Crop.photo offers several subscription plans with varying image credit allowances, enabling users to choose the plan that best fits their needs.
Results: Improved speed and precision
The automated image editing capabilities of Crop.photo with the integration of Amazon Rekognition has increased speed in editing, with 70% faster image retouching for ecommerce. With a 75% reduction in manual work, the turnaround time for new product images is reduced from 2–3 days to just 1 hour. Similarly, the bulk image editing process has been streamlined, allowing over 100,000 image collections to be processed per day using AWS Fargate. Advanced AI-powered image analysis and editing features provide consistent, high-quality images at scale, eliminating the need for manual review and approval of thousands of product images.
For instance, in the ecommerce industry, this integration facilitates automatic product detection and precise cropping, making sure every image meets specific marketplace and brand standards. In sports, it enables quick identification and cropping of player facial features, including head, eyes, and mouth, adapting to varying backgrounds and maintaining brand consistency.
The following images are before and after pictures for an ecommerce use case.
For a famous wine retailer in the United Kingdom, the integration of Amazon Rekognition with Crop.photo streamlined the processing of over 1,700 product images, achieving a 95% reduction in bulk image editing time, a confirmation to the efficiency of AI-powered enhancement.
Similarly, a top 10 global specialty retailer experienced a transformative impact on their ecommerce fashion marketplace workflow. By automating the cropping of over 3,000 on-model product images monthly, they boosted their retouching team’s productivity by over 70%, maintaining compliance with the varied image standards of multiple online marketplaces.
Conclusion
These case studies illustrate the tangible benefits of integrating Crop.photo with Amazon Rekognition, demonstrating how automation and AI can revolutionize the bulk image editing landscape for ecommerce and sports industries.
Crop.photo, from AWS Partner Evolphin Software, offers powerful bulk processing tools for automating image cropping, content resizing, and listing image analysis, using advanced AI-driven solutions. Crop.photo is tailored for high-end retailers, ecommerce platforms, and sports organizations. Its integration with Amazon Rekognition aims to streamline the image editing process for clients, providing speed and accuracy in the high-stakes environment of ecommerce and sports. Crop.photo plans additional AI capabilities with Amazon Bedrock generative AI frameworks to adapt to emerging digital imaging trends, so it remains an indispensable tool for its clients.
To learn more about Evolphin Software and Crop.photo, visit their website.
To learn more about Amazon Rekognition, refer to the Amazon Rekognition Developer Guide.
About the Authors
Rahul Bhargava, founder & CTO of Evolphin Software and Crop.photo, is reshaping how brands produce and manage visual content at scale. Through Crop.photo’s AI-powered tools, global names like Lacoste and Urban Outfitters, as well as ambitious Shopify retailers, are rethinking their creative production workflows. By leveraging cutting-edge Generative AI, he’s enabling brands of all sizes to scale their content creation efficiently while maintaining brand consistency.
Vaishnavi Ganesan is a Solutions Architect specializing in Cloud Security at AWS based in the San Francisco Bay Area. As a trusted technical advisor, Vaishnavi helps customers to design secure, scalable and innovative cloud solutions that drive both business value and technical excellence. Outside of work, Vaishnavi enjoys traveling and exploring different artisan coffee roasters.
John Powers is an Account Manager at AWS, who provides guidance to Evolphin Software and other organizations to help accelerate business outcomes leveraging AWS Technologies. John has a degree in Business Administration and Management with a concentration in Finance from Gonzaga University, and enjoys snowboarding in the Sierras in his free time.
