The past few years have seen huge breakthroughs in the use and application of artificial intelligence -- and AI holds major promise for people around the world. AI already powers Google's core products that help billions of people every day. Whether it's asking for movie times, finding the nearest doctor or finding better routes home -- our work in AI is centered on making everyday experiences more helpful. We've been developing AI for more than two decades. Some of our most popular products at Google -- like Lens and Translate -- were built entirely using artificial intelligence technologies like optical character recognition and machine learning.

The extraordinary technological advances have enabled the development of numerous helpful tools and techniques to alleviate human effort. Automatic Number Plate Recognition (ANPR), one such technology, is quickly gaining global prevalence and offers an abundance of advantages. It recognizes license plates and can be used for traffic enforcement, parking management, and many other activities depending on user demands. ANPR systems are highly reliable and built with cutting-edge technologies like artificial intelligence (AR), enabling them to be precise and functional. Thus, this blog post will discuss some key aspects of how the ANPR system works to provide you with a clear understanding of the mechanics of the ANPR system.

[Courtesy of Upstage]SEOUL -- Hanwha Life Insurance, one of the major life insurance companies in South Korea, adopted optical character recognition solution capable of processing insurance claim documents such as medical expense receipts. The solution created by domestic artificial intelligenc

Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images, such as scanned documents, photographs, bills, etc. The process involves analyzing the image and identifying the individual characters within it and then converting those characters into machine-readable text. OCR software can be used to automate tasks such as document scanning, business automation, and accessibility technology. OCR software uses complex algorithms and pattern recognition techniques to identify and extract text. OCR technology has evolved over time and now it has the ability to recognize text in multiple languages and different fonts.

Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.

Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the \emph{semi-targeted} scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.

Text-to-speech (TTS) and voice conversion (VC) are two different tasks both aiming at generating high quality speaking voice according to different input modality. Due to their similarity, this paper proposes UnifySpeech, which brings TTS and VC into a unified framework for the first time. The model is based on the assumption that speech can be decoupled into three independent components: content information, speaker information, prosody information. Both TTS and VC can be regarded as mining these three parts of information from the input and completing the reconstruction of speech. For TTS, the speech content information is derived from the text, while in VC it's derived from the source speech, so all the remaining units are shared except for the speech content extraction module in the two tasks. We applied vector quantization and domain constrain to bridge the gap between the content domains of TTS and VC. Objective and subjective evaluation shows that by combining the two task, TTS obtains better speaker modeling ability while VC gets hold of impressive speech content decoupling capability.

Apple's website says the feature is initially only available for romance and fiction books, where it lists two available digital voices: Madison and Jackson. The service is only available in English at present, and Apple is oddly specific about the genres of books its digital narrators are able to tackle. "Primary category must be romance or fiction (literary, historical, and women's fiction are eligible; mysteries and thrillers, and science fiction and fantasy are not currently supported)," its website reads.

Text to speech is a process to convert any text into voice. Text to speech project takes words on digital devices and convert them into audio with a button click or finger touch. Text to speech python project is very helpful for people who are struggling with reading. To implement this project, we will use the basic concepts of Python, Tkinter, gTTS, and playsound libraries. The objective of this project is to convert the text into voice with the click of a button.

A multimodal AI story teller, built with Stable Diffusion, GPT, and neural text-to-speech (TTS). Given a prompt as an opening line of a story, GPT writes the rest of the plot; Stable Diffusion draws an image for each sentence; a TTS model narrates each line, resulting in a fully animated video of a short story, replete with audio and visuals.