How AI is empowering Brand Compliance
The fusion of Machine Learning, OCR, and NLP Models.
Welcome to the realm of compliance, where rules and technology harmoniously intersect to uphold the integrity of brands. Compliance plays a vital role in maintaining a consistent and authentic brand experience, safeguarding consumer trust and loyalty.
In this journey, we will explore the importance of compliance and how we created a fusion of Machine Learning, OCR, NLP models, and our Mathematics expertise to figure out the problems.
What is a Brand?
A brand refers to the overall perception and image of a company, product, or service in the minds of consumers. It encompasses various elements such as the company's name, logo, design, messaging, values, and customer experiences. A brand represents the unique identity and reputation of a business, distinguishing it from competitors in the market.
Compliance, in the context of branding, refers to adhering to specific guidelines and standards set by a company to ensure consistency and integrity in the representation of its brand across different channels and touch-points. These guidelines typically cover aspects such as logo usage, typography, color palette, imagery, voice and tone, and other visual and verbal elements.
A small dig into what happened with Volkswagen
VW marketed their diesel vehicles as eco-friendly and compliant with strict emission standards. However, it was later discovered that the software in their cars could detect when the vehicle was undergoing an emissions test and adjust the engine's performance to meet the requirements. In regular driving conditions, the vehicles emitted nitrogen oxide (NOx) pollutants well above the acceptable limits, contributing to air pollution and environmental harm.
The scandal had significant consequences for Volkswagen, including substantial financial penalties, a damaged reputation, and legal ramifications. The brand's integrity and trust were severely impacted, as customers and stakeholders felt deceived and betrayed by VW's actions.
This is how serious things can happen in cases of compliance breach.
How is it done presently?
Currently, compliance validation is primarily performed by experienced professionals with in-depth knowledge of the specific guidelines and regulations governing brand compliance. These experts manually review creative content, assessing various elements such as typography, colors, imagery, branding consistency, legal requirements, and other relevant factors. They compare the content against predefined compliance guidelines and identify any deviations or violations.
Open any e-commerce app or visit any well-known brand's website and observe the consistency across their images.
After observing the same we analysed some trends :
Logos are usually placed at corners or centre of the creative.
T&C is very less font size and placed usually at a bottom corner.
Overall 40-50% white-space to get better visibility.
Total number of character limitations.
In some ad-spaces we also observed some UI portion being acquired, so in that place nothing should be existing.
Use of certain colors.
No mention of any designations etc.
Gender and diversity neutral policies.
No support of alcohol.
The fusion of ML, OCR, and NLP?
So after analysing the problem we realised the need of the following and broke the problem into following pieces :
Segregating different pieces of image, heading, sub-heading, call-to-action, terms-and-condition, logos.
Counting text, doing analysis on it etc.
Asking questions to the image or text inside it on certain things like designation mentions, title or sentence case etc.
To analyse the above problem we did the following :
Using OCR technology to figure out all the text inside the image.
Using machine learning to train from each account creatives and identifying different labels.
Using NLP models to identify some things like designation, religion, alcohol bottles, etc.
Problems in combining three different technologies
While combining three different tech’s the major problem was identifying their super powers and playing to strengths. And all these will have errors and combining three different techs will cause increase of error and multiplication of those.
Super powers elaborated :
OCR is great in identifying all the text inside the creative. But won’t be able to classification.
ML or image classification will be good at identifying major pieces but won’t know what is inside it.
NLP models will help us in analysing some important rules. But not much helpful in classification as just text won’t give the whole perception of a creative.
So the process followed :
Gather all the information from OCR.
Take out the data from our image classification model.
Try intersecting the image classification data and intersecting the data from OCR.
Tweak a couple of values around overlap percent and all.
Iterate, test and repeat.
After separating and mixing all the details the final thing was to put all this behind an engine for us to evaluate. This was using a JSON rule engine library, and extending it to run the things we need.
We added a workflow of two types of rule parts in general :
filter : Filter elements based on any parameter or rule.
aggregate : Aggregate the array we got from filter or already existing and get sum, or any other operations
A small example was having a requirement where elements do not intersect with a Person’s image. So for this we wrote a small piece to detect if a single position is intersecting with the other. Sample code below, with the rule engine JSON attached.
After all this is done some of these cost should always be a factor in any project to make it business viable :
Machine learning and these NLP models are slightly on the costlier side but there are ways to reduce some cost.
Try using batch predictions instead of an online prediction, although that also depends on scale and user experience need to be provided.
Building own models and deploying the same behind kubernetees.
Future scope and story
Rocketium aims to become the ultimate destination for comprehensive creative validation services. As a one-stop solution, we empower brands to effortlessly validate their creatives for advertising on any platform. Our platform eliminates the need for constant back-and-forth between teams by efficiently handling minor ad modifications. By entrusting us with the process, designers can save valuable time and focus on their core tasks, confident that their creatives will meet the necessary requirements seamlessly. With Rocketium, brands can streamline their validation workflows and ensure a hassle-free experience from start to finish.