Our second example deals with a more challenging problem: the recognition of hand-printed letters of the alphabet. The characters that people print in the ordinary course of filling out forms and questionnaires are surprisingly varied. Gaps abound wherecontinuous lines might be expected; curves and sharp angles appear interchangeably; there is almost every imaginable distortion of slant, shape and size. Even human readers cannot always identify such characters; their error rate is about 3 per cent on randomly selected letters and numbers, seen out of context.
– from Oliver G. Selfridge & Ulric Neisser. PATTERN RECOGNITION BY MACHINE . In Computers & thought, Edward A. Feigenbaum and Julian Feldman (Eds.). MIT Press, Cambridge, MA, USA, 1963. pp. 8-30.
TL;DR: A lifetime subscription to TextSniper for Mac is on sale for £2.92, saving you 42% off list price. TextSniper is a Mac app that lets you extract text from sources like images, YouTube videos, PDFs, screenshots, or presentations. Thanks to advanced OCR (optical character recognition) technology, TextSniper can scan and recognise the text within any digital image, video, or document. It will then copy it, allowing you to paste the text directly into an editable format, like a note, text, or even Google Doc. It can also turn recognised text into speech, in case there's a word or phrase you need to be pronounced, and scan barcodes and QR codes and turn them into text..
Optical character recognition (OCR) is a sort of image conversion that basically extracts text from a given image, a document photo, etc. Various applications and technologies, such as Adobe Acrobat and the ML-based tool, such as Tesseract OCR, have been developed to aid with this process. In this article, we will go over tasks performed in the OCR method. Thereafter, we will look into MMOCR, a Python-based application that centralizes all OCR-related operations. Below are major points listed that are to be discussed in this article. Let's first discuss text detection.
FinTech refers to any idea or innovation that improves or optimizes the way individuals or companies conduct financial activities. Early FinTech concentrated on developing add-on products to complement existing financial services. This combination of finance and technology has spawned a slew of valuable goods and services that redefine financial services and make them more accessible to the general public. Some of these products and services include insurance aggregators, mobile wallets, AI investment management advisers, peer-to-peer (P2P) lending and crowdfunding tools, and platforms for trading financial assets. The cutting-edge solutions that contributed to such technologies include Blockchain, Deep Learning, and Artificial Intelligence (AI).
OrCam MyEye PRO is a wearable assistive technology device for people who are blind, visually impaired or have reading challenges. It's lightweight, finger-size and magnetically mounts on eyeglass frames. The device instantly reads aloud any printed text (books, menus, signs) and digital screens (computer, smartphone), recognizes faces, and identifies products/bar codes, money notes and colors – all in real time and offline. The interactive Smart Reading feature enables users to tailor their assistive reading experience, and Orientation assists with guidance and identification of objects. Newly released "Hey OrCam" enables control of all device features and settings hands-free, using voice commands.
Many believe that artificial intelligence is a futuristic concept that we only see in sci-fi movies with humanoid robots and holograms. However, it is becoming rooted in our reality, affecting various fields and groups, including persons with disabilities. Accessibility and inclusivity are genuinely revolutionized, thanks to artificial intelligence! People with disabilities can substantially enhance their daily life thanks to AI technology solutions. We've already shown how smartphones can be tools for people with vision impairments.
Most of the time, the raw data that we need for our data science project is not organized in a neat, well-structured, and insightful table. Rather, this is sometimes stored as text in a scanned document. Words in the document must then be extracted one by one to form a text formatted data cell. This is the task performed by Optical Character Recognition (OCR). As you read the words of this article, be it text or number, your eyes are able to process them by recognizing light and dark patterns that make up characters (e.g., letters, number, punctuation marks, etc.).
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.