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I n my previous  article, we saw how one can utilize a prebuilt model to read data from a sales receipt. In this article, we will learn to create our own ML model, train it, and then extract information from a sales receipt. Here custom model means a model which is completely tailored to meet a specific need or a use case. Steps involved To perform this end-to-end workflow, there are 4 major steps. Step 1 - Create Training Dataset For training a model we need at least 5 documents of the same type, which means if we are planning to analyze receipts, then we need at least 5 samples of the sales receipts. If we are planning to extract data from a business card, then we need to have at least 5 samples of a business card, and so on and these documents can be either text or handwritten. Step 2 - Upload Training Dataset Once the training documents are collected, we need to upload that to Azure Storage. To perform this step, one should have  Storage Account  created on the Azure portal and one

Nowadays, where almost everything is turning to online and virtual modes, a very common problem any organization is facing is the processing of receipts that were scanned and submitted electronically for reimbursement purposes.  Now for any claim or reimbursements to get clear, first those must reach to proper accounts department based on the organization and the sector, and one way to perform this activity is by manual intervention. A person or a team must go through all those digitally scanned receipts manually and filter them based on the departments or any other validation and eligibility criteria they may have. The situation becomes more tragic when the volume of such scanned receipts is too high. So, get rid of this manual effort, a lot many organizations have already opted for a solution that is AI-based, and lot many are in a process of doing so. Definitely, one can go for OCR, which is short for  O ptical  C haracter  R ecognization technologies to extract data but here the pr

Azure Web PubSub service, as its name says, it is based on publish-subscribe pattern and enables us to build real-time web applications.  Some of the popular examples where we can use this service is, for any chat-based applications, any collaboration application, like white boarding application. We can also use this service for any application which needs instant push notifications. In fact, there are many more example, we can think about.  The best part is, we can use Azure Web PubSub service on all the platforms which supports WebSocket APIs and it allows up to 100 thousand concurrent connections at any point of time. Components required to create a basic chat application: Instance of Azure Web PubSub Service Publisher application Subscriber application To know about how to create and use these components, I’ve created a complete video demonstrating these on my YouTube channel named Shweta Lodha. C# Code for Publisher and Subscriber: Below is the C# code for the respective