AI Food Label Reader: Unraveling the Mystery of Food Ingredients

In today’s health – conscious consumer landscape, people are paying more attention to food nutrition labels than ever. However, the complex terminology, tiny fonts, and perplexing chemical components on these labels often leave consumers feeling overwhelmed. Despite the rising prevalence of lifestyle – related diseases, such as obesity, diabetes, and heart disease, which are closely tied to unhealthy eating habits, deciphering food labels remains a daunting task for the average person. Take India as an example; although there are campaigns encouraging people to “read the label,” like “Label Padega India” and “Packet Ghumao, Pata Lagao,” it’s still not easy for ordinary consumers to understand the labels. Questions like “What does INS 322 mean?” “How much added sugar is too much?” and “Is this snack healthier than the one next to it?” are common.
The crux of the problem is that understanding nutrition information today demands time, expertise, or blind trust in marketing claims. This article will take you through how I built an AI – powered Food Label Reader – a tool that allows users to upload food packaging images and automatically extract structured nutrition facts and ingredient lists.

The Foundation: Real Data from Real Platforms

Even the smartest AI can’t solve a problem without the right data. One of the biggest hurdles in building reliable food label readers has always been the lack of good, diverse, and up – to – date packaging images in one place. Quick Commerce platforms have changed this situation. Today, platforms like Blinkit, Zepto, and Instamart are legally required to display clear packaging images, including nutrition facts and ingredient lists, for every product they sell. This regulation means that a treasure trove of real – world, fresh packaging data is right at our fingertips – updated daily, diverse, and messy, just like the real world. This has become the foundation for building and validating my tool.

System Architecture Overview

Building Pack2Facts involves combining Vision Models and LLMs into a seamless pipeline. The high – level flow is as follows:

Step – by – Step Flow

Image Upload

Users upload multiple packaging images of a product from Quick Commerce platforms. Each product listing includes a slideshow of several images, not just one. These may contain front packaging, branding shots, nutritional information, and ingredient lists all mixed together.

Image Classification (CLIP Zero – Shot Model)

Not all images are useful – how do we find the ones that matter? To accurately extract useful information, we first need to identify the relevant images: nutrition facts panels and ingredients lists. For this, we use CLIP, a powerful zero – shot Vision – Language model. Why CLIP? CLIP learns a shared space for images and text, enabling zero – shot classification. It matches packaging images to descriptions like “nutrition label” without retraining, making it perfect for messy, real – world data. The outcome is that CLIP automatically classifies images into: Nutrition Facts, Ingredients List, and Irrelevant images (branding, marketing). This ensures that only the correct images move forward to the extraction step.

Preprocessing for Better Extraction

Even after identifying the correct images (nutrition labels, ingredient lists), many real – world packaging images are printed with low contrast (e.g., red Coke labels with white text), blurry, or distorted by lighting or compression. This can reduce the accuracy of any Vision – Language Model trying to extract structured data. To handle this, we leverage traditional Computer Vision Techniques using OpenCV to apply GrayScale Conversion, Contrast Enhancement, and Image Sharpening. Without this step, even advanced models can fail to detect key elements, missing nutrient names or confusing grams with percentages.

Information Extraction with Gemma – 3 – 12b – it – qat

Once we have clean, preprocessed images, the next challenge is extracting structured information, such as nutrition facts (calories, fat, sugars, sodium) and RDA values, ingredient lists, and serving sizes. For this, I used Gemma – 3, a Vision – Language Model fine – tuned to handle tasks combining image understanding and natural language processing. It’s hosted through LM – Studio on my laptop, ensuring data security and privacy. Gemma – 3 VL model reads packaging images the way a nutritionist would – pulling out key facts from messy, unstructured labels.

Insight Generation: Summarizing Nutrition and Ingredients

Raw nutrition tables and ingredient lists, even if extracted cleanly, are still hard for an average person to interpret. Just giving numbers like Sodium: 15mg, Fat: 15g, and INS 322 isn’t enough. Most consumers need simple, understandable summaries to make smarter decisions without needing a background in nutrition science. We leverage open – source LLM models to summarize the nutrition and ingredients data – expanding on the ingredients, highlighting pros and cons of the food being consumed.

Conclusion

Today, data is everywhere, and with everything going digital, it’s becoming more abundant than ever before. Building a truly valuable product doesn’t start with using the largest models or chasing the latest trends. It starts with understanding the data – and the real challenges hidden within it. Understanding the data reveals why models fail – like how white text on red backgrounds confuses OCR – and guides smarter design decisions. The real strength lies in tying together the right technologies – both old and new – into a system that’s thoughtfully designed and purposefully built. In Pack2Facts, it wasn’t just about LLMs and Vision Models. Traditional image processing techniques – like grayscale conversion, contrast enhancement, and sharpening – played an equally critical role in boosting model accuracy and handling messy, real – world inputs. The best part is that Pack2Facts is completely built using open – source models and tools. As LLMs become smaller and more efficient – like the recently released Gemma 3n, capable of running even on mobile devices – we are moving toward a future where tools like this aren’t just for researchers or companies. They can be in the hands of everyday consumers – helping people make smarter, faster, healthier decisions right from their phones.

The Importance of Reading Food Labels

Reading food labels is an essential skill for maintaining a healthy diet. Food labels provide valuable information about the nutritional content of food products, including calories, fat, sugar, sodium, and other nutrients. By understanding this information, consumers can make informed choices about what they eat and ensure they are getting the nutrients their bodies need. However, many people find food labels confusing and difficult to understand. The complex terminology, tiny fonts, and perplexing chemical components on these labels often leave consumers feeling overwhelmed.
The rise of lifestyle – related diseases, such as obesity, diabetes, and heart disease, has made it more important than ever for people to be aware of what they are eating. These diseases are often linked to poor dietary habits and the consumption of ultra – processed foods. By reading food labels, consumers can identify foods that are high in added sugars, unhealthy fats, and sodium, and choose healthier alternatives.

How to Read Food Labels

Understanding how to read food labels is crucial for making healthy food choices. Here are some tips for decoding food labels:

Look at the Serving Size

The serving size is the first thing you should look at on a food label. It tells you the amount of food that the nutritional information is based on. Be aware that the serving size may not be the same as the amount you actually eat. For example, a serving size may be half a cup of cereal, but you may eat a full cup. If you eat more than the serving size, you will consume more calories and nutrients than listed on the label.

Check the Nutritional Information

The nutritional information on a food label includes calories, fat, sugar, sodium, and other nutrients. Pay attention to the amounts of these nutrients and compare them to the recommended daily values. For example, if a food product has 500mg of sodium per serving, and the recommended daily value is 2300mg, then one serving of this food would provide about 22% of your daily sodium intake.

Read the Ingredients List

The ingredients list on a food label shows all the ingredients used to make the product. Ingredients are listed in order of quantity, with the most abundant ingredient listed first. Look for foods that have simple, recognizable ingredients and avoid foods with a long list of artificial additives and preservatives.

Watch Out for Hidden Sugars

Added sugars can be hidden under many different names on food labels, such as high – fructose corn syrup, sucrose, glucose, and fructose. Be aware of these hidden sugars and try to limit your intake of foods that contain them.

The Benefits of Reading Food Labels

Reading food labels has numerous benefits for your health and well – being. Some of the key benefits include:

Making Informed Food Choices

By reading food labels, you can make informed choices about what you eat. You can identify foods that are high in nutrients like protein, fiber, and vitamins, and avoid foods that are high in added sugars, unhealthy fats, and sodium.

Managing Diet – Related Conditions

If you have a diet – related condition like diabetes, heart disease, or food allergies, reading food labels can help you manage your condition. You can avoid foods that may trigger symptoms or worsen your condition and choose foods that are more suitable for your dietary needs.

Maintaining a Healthy Weight

Reading food labels can help you maintain a healthy weight by allowing you to keep track of your calorie intake. You can compare the calories in different food products and choose options that are lower in calories but still nutritious.

Improving Overall Health

By making healthier food choices based on food labels, you can improve your overall health and reduce your risk of developing chronic diseases. A diet that is rich in nutrients and low in added sugars, unhealthy fats, and sodium can help you feel better, have more energy, and live a longer, healthier life.

Common Challenges in Reading Food Labels

Despite the benefits of reading food labels, many people face challenges in understanding them. Some of the common challenges include:

Complex Terminology

Food labels often contain complex terminology that can be difficult for the average person to understand. Terms like “insulin – dependent diabetes mellitus” or “partially hydrogenated oil” may not be familiar to everyone, making it hard to decipher what the label is saying.

Tiny Fonts

The font size on food labels can be extremely small, making it difficult to read the information, especially for people with visual impairments or those who don’t have reading glasses handy.

Perplexing Chemical Components

Food labels may list a variety of chemical components that are hard to pronounce and even harder to understand. These components may be additives, preservatives, or other substances used in food processing, and their safety and impact on health can be unclear.

Misleading Marketing Claims

Food manufacturers often use marketing claims on food labels to make their products appear healthier than they actually are. Claims like “natural,” “low – fat,” or “high – fiber” can be misleading and may not accurately reflect the nutritional value of the product.

How AI Can Help with Reading Food Labels

AI technology can be a valuable tool for helping people read and understand food labels. AI – powered food label readers, like Pack2Facts, can automatically extract structured nutrition facts and ingredient lists from food packaging images. This can make it easier for consumers to access and understand the information on food labels, even if they don’t have expertise in nutrition or food science.
AI food label readers can also provide additional insights and summaries of the nutritional information and ingredients in food products. This can help consumers make more informed decisions about what they eat and identify foods that are more suitable for their dietary needs and preferences.

The Future of AI in Food Label Reading

As AI technology continues to advance, we can expect to see even more sophisticated and user – friendly food label reading tools in the future. These tools may be integrated into mobile apps, smart devices, or even grocery store systems, making it easier than ever for consumers to access accurate and reliable information about the food they buy.
Furthermore, as more data becomes available on food products and nutrition, AI models can be trained to provide even more detailed and personalized recommendations based on individual dietary needs and preferences. This can help people achieve their health goals and maintain a balanced and nutritious diet.

Final Thoughts

Reading food labels is an essential skill for maintaining a healthy diet and making informed food choices. While there are challenges in understanding food labels, AI technology can be a helpful tool for overcoming these challenges and making it easier for consumers to access and interpret nutritional information.
By using AI – powered food label readers like Pack2Facts, consumers can gain a better understanding of what they are eating and make choices that are more aligned with their health goals and dietary needs. As AI technology continues to evolve, we can look forward to even more innovative solutions for improving our food – related decisions and overall health and well – being.