Don't worry. Not everyone knows it...

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

In this article, we will learn about how we can read or extract text from an image, irrespective of whether it is handwritten or printed. In order to read the text, two things come into the picture. The first one is  Computer Vision  and the second one is  NLP , which is short for Natural Language Processing. Computer vision helps us to read the text and then NLP is used to make sense of that identified text. In this article, I’ll mention specifically about text extraction part. How Computer Vision Performs Text Extraction To execute this text extraction task, Computer Vision provides us with two APIs: OCR API Read API OCR API,  works with many languages and is very well suited for relatively small text but if you have so much text in any image or say text-dominated image, then  Read API  is your option. OCR API  provides information in the form of Regions, Lines, and Words. The region in the given image is the area that contains the text. So, the output hierarchy would

When we are talking about a term environment along with Python, it is a context in which our Python application runs, or we can say that the Python program runs.   An environment consists of an interpreter and all the installed packages, which clearly means that one can have multiple environments on a single machine, or rather I would say, every Python application can have its own environment. Now the question is, why do we need such environments?   To know more about the virtual environment and how to create one using Visual Studio Code, watch out my recorded video on my YouTube channel named Shweta Lodha.

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