Source: Techeblog

Understanding the Image Recognition Technology Driving the Future of Shopping

If you have ever wondered how image recognition works on e-commerce websites like Amazon, then you are in luck. Amazon calls this feature Amazon Lens. Having the feature of Amazon Lens is wonderful, especially if someone is trying to find that particular item online or does not have the time to type out the product name. A user simply points their camera at a live image or picks something up from the Phone Gallery to find the product that is the closest match to it. The magic of Amazon Lens has three modes of scanning —

1. Image Recognition / Search
2. Barcode
3. Payment QR scanning

The last one is common across a host of Fintech products, hence I am going to focus this post on the inner working of image recognition and barcode scanning.

About Amazon Lens

By using Amazon Lens, users can take a picture of a product and search for it on Amazon using the Amazon app. In other words, this feature lets the user search for products on Amazon by simply taking a picture of that item. Once the photo has been scanned, the user can then use the app to find items that are similar to the one shown in the picture. It can be extremely useful for those who are out shopping and wish to see if there are any discounts on a product or if it is available on Amazon while out shopping.

Amazon Lens is simply activated by opening the Amazon app and selecting Lens. Users can then take a picture of the item they’re looking for, or upload an existing image from their device. When the app analyzes the image, it will identify any products that match the item. The user can then browse through the search results to find the product they’re looking for, or they can buy it directly from the app. Both Android and iOS users can use Amazon Lens to scan barcodes and QR codes as well as search for products using text or other visual features.

How does Image Recognition work?

To determine what an image represents, image recognition technology analyses the image and identifies the unique features within it. This is done by comparing them to a database of known features, to determine what it represents. In the same way, Amazon — an e-commerce platform, can use its image recognition system to recognize products in images and match them with items that can be purchased on the platform. Customers who upload images of products from Gallery will be able to search and find products on the platform based on what’s in the image. In addition, they will be able to identify the products in the image taken directly from their camera for identification.

Training

A large dataset of images with corresponding labels indicating what the images depict is necessary for the system to be trained in image recognition in this manner. As a result of this training process, the system can become familiar with various products, as well as their features. It can also identify them in new images as they arise. After the system has been trained, it can be used to identify products or features depicted in upcoming images in the future.

For image recognition, machine learning algorithms, pattern matching, and feature extraction are some of the most common techniques.

Blinking dots for in-image recognition

In the user interface of an image recognition camera, the dots indicate the parts of the image the system is analyzing to identify the objects or features. To determine what is depicted in the image, these dots are typically placed over the parts of the image that have been identified as important or relevant. In addition to the specific image recognition system being used, the number and distribution of the dots can vary depending on the complexity and resolution of the image.

It is possible to use the dots as a measure of the image recognition system’s relative confidence in analyzing the image. In areas where the system has more confidence in the image, the dots may be larger and more numerous, while in areas where the system is less confident or more ambiguous, the dots may be smaller and fewer. Users can use this information to understand how the image recognition system interprets the image, thereby making it easier for them to comprehend the analysis’s results.

Image Recognition for Barcode scanning

To match barcodes on physical products with those in a database, image recognition systems can analyze the barcode image and compare it to a database of known barcodes. To determine the series of numbers and letters encoded in a barcode, the image recognition system analyzes the pattern of black and white lines in the barcode when it is scanned. Based on this information, the software can search the database for a record matching the barcode, which will typically include details about the product.

In the process of producing or printing a Barcode, several aspects need to be considered — Data Encoding, Size & Resolution, Printing and Durability, Error Correction, and Symbology. Among these Data Encoding and Symbology are the most important. So, I will define those here further —

1. Data Encoding: For image recognition systems to read and interpret the data encoded by the barcode, they must be designed to encode the information it is meant to represent. To represent different digits or letters, black-and-white lines or bars are typically used of varying widths.
2. Symbology: It is important to note that there are many different types of barcodes, called symbologies, each of which encodes data differently. There are several barcode symbologies available for use, depending on the application and its requirements. Some examples include UPC-A, EAN, Code 39, Code 128, and QR codes.

Data encoding in barcode

Data encoding in a barcode is the process of representing digital data using a series of lines or bars of varying widths. In a barcode, data is encoded using a specific symbology. Many different symbologies can be used for barcodes, each with its own set of rules for encoding data.

Data is encoded in a barcode by mapping each digit or letter to a specific pattern of lines or bars. The patterns are typically designed so that they can be read and interpreted easily by image recognition systems, while still being compact so that a large amount of data can be encoded in a small amount of space.

The most common barcode symbology, UPC-A, represents each digit of the data with a pattern of seven bars and spaces, varying in width depending on how many digits they represent. Data is read from left to right, with the leftmost bar representing the first digit and the rightmost bar representing the last digit.

Other barcode symbologies explained:

1. UPC-A: In the United States, this is the most widely used barcode symbology for retail products. The data is encoded using bars and spaces, whose widths differ for different digits.
2. EAN: In addition to the UPC-A barcode symbology, this barcode symbology uses an additional digit to encode data instead of the bars and spaces used in UPC-A.
3. Code 39: Many industries utilize this barcode symbology, including manufacturing, healthcare, and government. It encodes alphanumeric data using bars and spaces.
4. Code 128: Generally used in shipping and logistics applications, this high-density barcode can encode a wide range of characters, including letters, digits, and special characters.
5. QR code: The two-dimensional barcode symbology uses a grid of squares to encode data. It can be read by smartphone cameras and is commonly used for mobile payments and event tickets.

To conclude, Amazon Lens is a revolutionary tool that revolutionizes how we shop online by utilizing image recognition technology. Users can search for and purchase products on Amazon by simply taking a picture of an item, or they can scan barcodes or QR codes for more information. Using this technology, online shopping can be even more efficient and convenient, and it is likely to become an increasingly important part of the e-commerce landscape in the future. A game-changer for online shoppers everywhere, Amazon Lens offers advanced image recognition capabilities.

That’s the end of this short yet hopefully insightful read. Thanks for making it to the end. I hope you gained something from it.

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