A Few months back, the student in class captured the image of the notes made by the other student in front of him and used iOS 15's recent text-recognition feature to highlight text, and copy and paste it into his notes. This instance was tweeted by @juanbuis, who shared the video of a student making the most of iOS 15's Live Text OCR feature. This cool OCR or Optical Character Recognition feature that the above student opts for is generally used to pull up the information from the text or documents and then convert it into the machine's language. Recently, the popular app developer Alessandro Paluzzi has also seen that Twitter is working on an OCR (optical character recognition) feature for the description of alt text. In his tweet, Alessandro Paluzzi shared the demonstration of how this twitter feature will function through a short video. At Dwarf AI we too want to make this super cool technology to be easily accessible by other businesses.

Early versions of OCR had to be trained with images of each character and could only work with one font at a time. Modern machine learning algorithms make the text recognition process more advanced and provide a higher level of recognition accuracy for most fonts, regardless of input data formats. Advances in machine learning (ML) have given a new impetus to the development of OCR, significantly increasing the number of its applications. With enough training data, the OCR machine learning algorithm now can be applied to any real-world scenario that requires identification and text transformation. For example, receipts scanning, scanning of printed text with the further conversion of it into synthetic speech, traffic sign recognition, license plate recognition, etc.

Your problem: you have a bunch of scanned images or PDFs but cannot make use of it because you cannot even select the text. Your solution: you just need to OCR your scans (saving the results into text-embedded PDFs) and then to upload those into tagtog. From there on, you can annotate (by just highlighting the text) and export your valuable data into machine-readable JSON. This is thanks to the Native PDF annotation built in tagtog. All your text selections can then be downloaded in JSON, which you can use to train your ML models!

Applying machine learning techniques to security solutions is one of the current AI trends. This article will cover the approach to developing OCR-based software using deep learning algorithms. This software can be used to analyze and process identification such as a US driver's license as part of a security system for verifying identity. OCR (Optical Character Recognition) technology is already used by machine learning companies for business processes automation and optimization, with use cases ranging from Dropbox using it to parse through pictures to Google Street view identifying different street signs to searching through text messages and translating text in real time. In this particular case, OCR can be used as part of an automated biometric verification system.

This article will take you through how these companies can automate several procedures like menu digitization or invoice processing that are traditionally done manually to save time and operational costs. We have all had moments when we suddenly crave a good dessert. Getting that big tub of ice-cream after a long day at work would've been an inconvenience a few years ago. But food delivery apps can get it to you at a lightning fast speed. With companies like DoorDash, DeliveryHero, GrubHub, FoodPanda, Swiggy, Zomato and Uber Eats competing for a greater market share in the food delivery market, adopting technology that aids companies to scale up their operations has become a necessity to stay relevant.

Have you ever faced challenges while creating user-oriented digital security algorithms? Designing a more efficient solution to replace the creation and maintenance of paperwork for numerous employees is certainly beneficial. However, even it the era of Data Science and Artificial Intelligence, reinventing security-related services is no easy task. Let's see the approach to develop software solutions with deep learning Optical Character Recognition (OCR) for processing US driver's licenses and IDs. This technology began with the scanning of books, text recognition and hand-written digits (NIST dataset).

Generic OCR programs are adequate for simple text scooping and editing. For example, with Microsoft OneNote, you can import a PDF or JPEG image and use the OCR tool to output text. General purpose OCR lacks the tools for your business to take full advantage of the tremendous labor-saving and machine learning capabilities that full-powered OCR provides. You need OCR that scales to your needs and is specific for your business context. Think of OCR as a data collection tool.

If you had to make a list of the major things that business leaders in nearly every industry are concerned about on a daily basis, tax preparation would undoubtedly be right at the top. It's a major source of concern – whether they're worried about the effects of tax reform, the consequences of making a mistake or the sheer volume of time and effort involved in preparation to begin with, it's something that many cite as one of the biggest challenges of running a business, year after year. But what if there was a way to streamline the tax preparation process? If there was a solution that not only allowed you to automate a lot of the administrative side of tax preparation, but also reduce the possibility of human error and extract relevant information from W2s and other documents in real-time, that would be a solution worth investigating, right? The good news is that this solution already exists: it's called OCR and if your business isn't already using it to help manage your tax documents, there are a number of compelling reasons as to why now would be an excellent time to start.