This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

Neural text-to-speech (TTS) models are successfully used to generate high-quality human-like speech. However, most TTS models can be trained if only the transcribed data of the desired speaker is given. That means that long-form untranscribed data, such as podcasts, cannot be used to train existing models. A recent paper on arXiv proposes an unconditional diffusion-based generative model. It is trained on untranscribed data that leverages a phoneme classifier for text-to-speech synthesis.

A text-to-speech TikTok voice made by Disney that made users sound like Rocket Raccoon does not allow users to'say' words like "gay", "lesbian", or "queer". Numerous posts by users showed the feature failing to say the LGBTQ terms before it was quietly changed to allow the words. Words like "bisexual" and "transgender", were allowed by the feature. Originally, Rocket's voice would skip over the words when written normally but would be pronounced phonetically if a user wrote "qweer", for example. Attempts to make it read text that contained only the seemingly-prohibited words resulted in an error message saying that text-to-speech was not supported by the language chosen.

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

Instagram was clearly trying to court TikTok users when it launched its short-form video format called Reels. Now, it has introduced two features already widely popular on TikTok, perhaps in hopes that they can convert those who've been hesitating to use Reels due to their absence. One of those tools is text-to-speech, which provides a robotic voiceover for videos. When a user types in text for their videos, they'll now be able to get an auto-generated voice to read it out loud by accessing the feature living inside the Text bubble on the lower left corner of the screen. They then have to choose between the two available voice options before posting their video. While text-to-speech will make Reels more accessible, it's also popular on TikTok just because some find a robotic voice narrating their activities a funny addition to their content.

The benefits of digitizing a company's documentation are numerous, and converting papers to PDF files for archiving has been a common practice for years. Still, Artificial Intelligence's new horizons are giving archives a new lease of life, converting them into data sources. Fiscal records, contracts, communications, invoices, and any other documents related to the type of business are filed in every firm. Document digitization is widely used to make physical spaces more efficient and allow consultation based on criteria entered manually during the digital preservation of paper supports. Even though a process has existed for years, digital development may offer new value and innovation.

Today we are accessible to a mountain of intelligent technologies. And no doubt that computer vision stores a vital space among all of them. When we talk about computer vision, the foremost application that we think of is Image Recognition. But indeed, a computer vision also encompasses OCR (Optical Character Recognition) algorithm, which allows seamless computer operations. In this article, we will discuss the origin, advancements, OCR tasks, and OCR industry applications that are enriching the OCR Pipeline.

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilised efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.

If your business is driven by data, Optical Character Recognition (OCR) -- as most of us know it -- is not the answer. For those of you who view OCR as an industry staple for document processing, let me explain. OCR as a technology has been around for ages and it still has its place in processing unstructured document formats like PDFs, images, and other text formats that cannot be edited digitally. Users can quickly convert those files into editable documents. In short, it's a terrific technology for enabling you to edit and search for files that may have been "frozen."

By 2022, robotics trends and forecasts will improve the global technology industry. The pandemic presented both challenges and opportunities for logistics and supermarket robotics companies. Unexpected supply chain pressures and product shortages have highlighted the need to improve supply chain efficiency. Various industries have also suffered from labor shortages caused by health and safety regulations. The lessons learned in 2021 can be applied to the goals and trends of the robotics industry in 2022.