Handwriting Recognition API
Have you ever had to wait for the waiter?
If you are anything like the average restaurant-goer, then you’ve probably faced this problem. One of the greatest disadvantages of dining out, being patient while waiting for the person designated to come and take your order, can indeed feel very harrowing.
Further, once the waiter does arrive, you might have to hurry and give your orders; even then, there’s a certain waiting period between the actual ordering and the arrival of the dishes for your consumption. Such unnecessary waits can actually ruin the mood of dining out.
What’s more, longer wait times actually contribute towards decreasing the operational efficiency of the restaurant in question. The more a customer has to wait, the more they are likely not to come back a second time. Further, longer wait times essentially mean the restaurant is serving fewer people in a given amount of time, which naturally translates to lower revenues.
In order to solve the problem of long wait times and introduce better efficiency to the operation of restaurants in Germany, we’ve introduced the Handwriting Recognition API.
What It Is
The Handwriting Recognition API is a novel approach towards solving the problem of taking orders at a restaurant. The API can efficiently recognize handwritten notes, convert them to digital equivalents, and structure the same into orders that can be processed quickly.
The API is meant to be integrated into the form of a mobile application user can operate from the comfort of their smartphones. Just imagine going into a restaurant, taking your seat at the table, and ordering whatever you desire directly on your mobile.
Using this technology, you don’t even have to type in the order. You can simply scribble away in free form handwriting, and the app will recognize the handwritten note and convert it into a digital order to be processed by the restaurant.
Isn’t that simple!
But how does this seemingly intuitive app actually work behind the scenes? What technologies does it utilize? In order to find the answer to these questions, we must delve deeper into the inner workings of this technology.
How It Works
The fundamental concept behind the handwriting recognition API is that of convolutional neural networks that work for detection, recognition, and processing of the images that make up the handwritten notes.
Convolutional neural networks(CNNs) are essentially a type of deep learning neural system. CNN's are the behind-the-scenes magic behind most image recognition technology and contribute significantly to image classification. So from getting tagged in your Facebook photo to ordering your next meal via a smart device, you have CNN's to thank for it.
A typical CNN has multiple convolutional layers that take input and convolve it to give the required output. Mostly, CNNs are best used for processing two-dimensional images.
Originally inspired by the design of the visual cortex, CNNs require very small amounts of preprocessing and hence, are extremely efficient. They can be used in a wide variety of image recognition tasks like self-driving cars, auto-navigation systems, and similar applications.
So, how was this technology implemented in the handwriting recognition API?
Time to take a sneak peek.
The following are some of the concepts and tools that have been used in the creation of the API.
Flask is primarily a web framework that has been developed using the Python language; classified as a micro-framework due to its non-dependence on particular tools and libraries, the Flask server is a lightweight and efficient method that can enable the running of applications on the client-side of things pretty efficiently.
Image compression is another of the techniques that have been utilized to improve the performance of the Handwriting Recognition API. Using image compression techniques can enable the processed images to require lesser bandwidth and take up less space on the devices. This naturally facilitates edge-computing capabilities and makes for a faster, seamless experience.
Tensorflow is a free and open-source dataflow and differentiable programming library that has been developed by the Google Brain team. The Tensorflow library can be effectively used in creating machine learning applications. The convolutional neural network that forms the backbone of the Handwriting Recognition API is based on the Tensorflow library.
The above three technologies form the operational structure of the Handwriting Recognition API.
IN the 21st century, efficiency is the watchword of the day. You either step up or fall behind. After all, we live in the digital age...so why shouldn’t restaurants go the digital way?
The Handwriting Recognition API provides a unique solution to the problem of ordering from a restaurant. The appropriate use of this API in mobile applications that aid ordering can vastly improve the restaurant experience for customers.
As with everything, we at SDS believe that technology can be the savior of humanity if used wisely. AI systems that learn unlearn, and relearn can literally shape and change the way we interact with the world.
Keeping the above view in mind, we have created the Financial Product Recommendation system for our client Citigroup, which is the 4th largest bank in the world. Allowing the customers to choose from a plethora of financial products, our recommendation system utilizes high-level Artificial Intelligence to make sure that the customers get the products that they deserve.
So, how did this system come to be, and what problems does it actually solve? We are going to take a deep look at all of this and more in the following article.
So, sit back, relax, and let’s get on with the journey into a smart financial future.
But before all of that, what exactly do we mean by Financial Products?