Researching and analyzing thousands of menu cards from thousands of restaurant locations in a given geographical area is a daunting task. The process is long and can be tedious, involving multiple steps.
You drive over to a competitor’s restaurant and photograph the menu, drive back, manually enter the contents and structure of the menu into a spreadsheet, construct a usable database, and finally, are able to use the data to your advantage. Do this a hundred or thousand times over and you will have a valuable amount of data.
The man hours it costs to do this and the amount of time it will take before you have a solid amount of data is inefficient and certainly not cost-effective. Well, what if you take a different approach? We live in a modern age, there must be a way of having a computer do all the work for us, right?
You would be right! In this blog, we will explain how you can automate the process of menu card scanning using Optical Character Recognition (OCR) and Machine Learning.
What is Menu Card OCR?
Menu card OCR refers to the technology and process of scanning menu cards and extracting data, further converting it into a searchable format.
This is done through the following steps:
- You upload a photo of a menu card to the API.
- The API scans the photo and clarifies its content with blur and glare detection and correction.
- The image is then read with the help of OCR, and extracted into a raw TEXT-file.
- This TXT-file is converted into a JSON-format, which allows the data to be contextualized and categorized.
- You receive the JSON output, which can be deployed in data management software and is easily and automatically transferred into research plans.
The JSON output is automatically structured into predetermined data presets, which allows you to contextualize all data on the menu card. This means you don’t have to place every item into the context slot on a spreadsheet yourself.
How was Menu Data Extraction Performed Before OCR?
Before the advent of OCR technology for menu data extraction, the process involved a more hands-on approach and a series of manual inefficient steps. Good market research is crucial because it provides you with a continuous and current understanding of your competitors’ actions.
By staying informed about their pricing strategies and the introduction of new menu items, among other pertinent details, you gain valuable insights that can significantly enhance your competitive edge.
Gathering data on competitors or other restaurants in your neighborhood before automation would involve a series of manual and inefficient steps.
- Take a photo of a restaurant menu card. This photo can be taken in haste or subject to low lighting.
- Data on the photo is manually transferred into a spreadsheet, including sections such as name, course, ingredients, price, allergy warnings, and more. This information is combined with the name and address of the restaurant.
- The spreadsheet is then processed by hand into a database of multiple restaurants.
- This database is scoured for useful information on which conclusions about for example pricing can be made.
Essentially, if you wish to keep ahead of the competition, you would need to do this on a regular basis. This means this painstaking process starts over every month, or sometimes even every week, which makes it a costly and time-consuming endeavor. This type of high volume and frequency work will expand your crew requirements, which might tempt you to look at back-office outsourcing to low-wage countries. Understandable, but maybe there is another way.
How Can it Be Done with Menu OCR?
Automating your approach would be the best way to start. You would still need to provide a photo or other form of image of the menu card, but that would basically be it. An AI could take it from there. You provide the input, the AI provides the output. The following steps should be taken:
- You upload a photo or PDF of a menu card.
- The photo is automatically clarified and corrected.
- The image is then read with the help of OCR, and converted into raw text.
- This text is converted into a format that allows it to be contextualized and categorized using machine learning algorithms.
- You receive the output, which can be deployed in data management software and is easily and automatically transferred into research plans.
This process is fully automatic, accurate, fast and most of all modern. The API is supported by an AI that has been trained with practical examples and continues to learn over time. It allows you to keep everything within your own control, without being dependent upon error-prone manual entry.
How Does the Technology Behind Menu Card OCR Work?
With each step taken in the menu card OCR process, a piece of technology is triggered into action. It is this collaboration between different types of software that automates the process and makes it effective. Here’s how each piece of software works together to form the output you need:
- An easy-to-use GUI (Graphical User Interface) gives you access to the camera within the app, allowing you to take and upload a picture or upload a PDF. When needed, documents are automatically corrected on perspective and blur- and glare detection is used.
- The API (Application Programming Interface) takes it from here. This is the service that does all of the work and moves between the back end, where the work is done, and the front end, which is presented to you with the GUI. The API links the upload to the trained neural network that will determine what characters and data are on the image.
- Optical Character Recognition (OCR) is used to determine what pixels on a picture constitute readable text. Any text that is discernible in the photo is extracted into a simple digital TXT-format. The photo has now been transformed into digital data.
- The brain behind this process is a neural network in the form of an artificial intelligence (AI), which has been trained not only in identifying the text on an image, but is also able to make a reasonable assumption about what the context of specific text is. If a menu has a specific layout with multiple subsections, then the AI is able to automatically determine which item belongs to which section and what price belongs to it. This form of deep learning allows menu card OCR to go way further than simply turning an image into text.
- Using the contextualizing capacities of the neural network, a JSON-format is formed. JSON is suitable for data contextualization and can automatically group and link data points, both with existing database entries or simply as idiosyncratic data. This makes it ideal for all research purposes.
What Can you Use Menu Card OCR for?
Menu card OCR offers a range of practical applications, leveraging its ability to convert the content of a menu card into digital text. With this technology, you can enhance customer experiences, optimize business operations, and develop data-driven insights within the food and hospitality industry.
The result is data that you can use for your own specific aims. It is perfect for the following purposes:
Market research
Quickly contextualize what restaurants and bars from your specific target group are offering and at which price, but also how that might change over time. It can be for exploratory, descriptive or casual purposes and have the goal of determining a business strategy, competitiveness or simply for research purposes.
Competitor analysis style
With an accurate overview of menu offerings, pricing and changes of your direct competitors, you can quickly adapt and be one step ahead of the competition at all times. This empowers businesses to stay agile by promptly adapting their selection based on up-to-date insights gleaned from competitor menus.
Geographical pricing analysis
By singling out a specific area or region, you can use the data to set up research into pricing trends of restaurants and bars in the vicinity. You can adapt your own pricing scheme with accurate, contextual data. With a data-driven approach, businesses are empowered to conduct in-depth research and adjust their pricing strategies based on accurate and contextual information, aligning their offer with local market dynamics.
Large-scale menu digitization
This process is fully automatic, accurate, fast and most of all modern. The API is supported by an AI that has been trained with practical examples and continues to learn over time. It allows you to keep everything within your own control, without being dependent upon error-prone manual entry.
Use Cases for Menu Card OCR
So who exactly benefits from menu card OCR? Although you’ll find that the possibilities of using this data are near endless, we’ll provide you with three use cases:
Restaurant owners
Large restaurant chains need to keep track of what competitors in the area are doing. This is especially true when you drive a format that is meant to be competitive and consequently has many adversaries in a local area.
Whether you are challenging the market in terms of pricing, originality of menu, variety or any other format that involves what is on the menu card, a constant eye on what the competition is doing is paramount. It will give you competitive advantage over the rest.
The best way is to plan a monthly photo of relevant competitor menu cards in the vicinity. If you immediately take the photo in the app, you can instantly upload the image and it is processed directly. Say you wish to process a thousand menu cards per month in a large urban area and you need to quickly access the data.
Every time an employee uploads a picture, it is converted into JSON data within seconds. Your database can thus be kept up to date at any time. This means you can for instance safely boast the cheapest menu in the area or country, for you will know it to be true.
Food delivery platforms
Especially in times of COVID-19, the predominance of delivery platforms has grown. If you wish to compete in this sometimes saturated market, it is clear that you need an easy way of onboarding. However, it is just as important to keep your platform up to date.
To compete, you need to offer the delivery of hundreds of restaurants and bars, all of which adapt their menus with increasing regularity. You would need a fast way of processing all restaurant menus, so that none of the information on your platform is outdated.
A quick way is to have participating restaurants upload a photo of each new menu card to our API. With menu card OCR each menu is scanned and read, extracting all relevant data into a JSON-format. This format allows you to swiftly and automatically update any restaurant menu listing on your delivery platform. It is an excellent way to keep both participating restaurants and of course your customers happy.
Market research agency
If you aim to serve the restaurant industry with accurate data, you need to instigate an effective way of gathering that data. Whether it concerns a simple SWOT analysis or menu optimization for a restaurant, you should be able to offer your clients accurate and swift advice based on a thorough investigation.
Of course, you have employees that should be able to manually gather data, or you have chosen to outsource this back office work to low-wage countries. Still, gathering data is more of a computer’s job, wouldn’t you say?
Using an API to take over this work results in a more cost efficient, more accurate and faster process. Transforming a menu card into usable, segmented data is done by menu card OCR within a matter of seconds, whereas an employee will take longer to enter the data into segmented spreadsheets.
