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optical character recognition


Top 30 NLP Use Cases: Comprehensive Guide for 2021

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Natural language processing (NLP) is a subfield of AI and linguistics which enables computers to understand, interpret and manipulate human language. Although NLP faces different challenges due to the difficulty of human language, this did not become an obstacle in the face of its growth. The global NLP market was estimated at $5B in 2018 and is expected to reach $43B by 2025, and this exponential growth can mostly be attributed to the vast use cases of NLP in every industry today. You may already be familiar with many NLP applications such as autocorrection, translation, or chatbots. However, NLP is the cornerstone of numerous applications we use every day without even noticing.


The Machine Learning Overview -- Part I

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Machine Learning, DeepLearning and artificial intelligence algorithms, in general, are attracting increasing attention in various industrial and social fields. However, many interesting algorithms were developed a few years ago.


Best Scanners of 2021: Fujitsu, Canon, Kodak, Epson, and more

ZDNet

Scanning is one of those technologies that seem like if you need it, you know you need it. But as it turns out, scanning is also a technology where you may not know you need it until an urgency hits, and you need it right this minute. In addition to the urgent personal or business need that comes from moving a paper document from one location or another, scanning can be very helpful for document archiving and search, reducing clutter and physical space, reducing the time it takes to file documents, faster document preparation, and disaster management. I've personally benefited from using scanning during disaster management. The classic case was when my family had to evacuate during a hurricane and yet I had incredibly critical family business that required accessing certain years-old documents.


Highly accurate AWS machine learning based handwritten document scanner – IT Brief New Zealand

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It uses Amazon Textract, a machine learning service that automatically extracts text, handwriting, and data from scanned documents to provide highly …


HCR-Net: A deep learning based script independent handwritten character recognition network

arXiv.org Artificial Intelligence

Handwritten character recognition (HCR) is a challenging learning problem in pattern recognition, mainly due to similarity in structure of characters, different handwriting styles, noisy datasets and a large variety of languages and scripts. HCR problem is studied extensively for a few decades but there is very limited research on script independent models. This is because of factors, like, diversity of scripts, focus of the most of conventional research efforts on handcrafted feature extraction techniques which are language/script specific and are not always available, and unavailability of public datasets and codes to reproduce the results. On the other hand, deep learning has witnessed huge success in different areas of pattern recognition, including HCR, and provides end-to-end learning, i.e., automated feature extraction and recognition. In this paper, we have proposed a novel deep learning architecture which exploits transfer learning and image-augmentation for end-to-end learning for script independent handwritten character recognition, called HCR-Net. The network is based on a novel transfer learning approach for HCR, where some of lower layers of a pre-trained VGG16 network are utilised. Due to transfer learning and image-augmentation, HCR-Net provides faster training, better performance and better generalisations. The experimental results on publicly available datasets of Bangla, Punjabi, Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu, Marathi, Nepali and Arabic languages prove the efficacy of HCR-Net and establishes several new benchmarks. For reproducibility of the results and for the advancements of the HCR research, complete code is publicly released at \href{https://github.com/jmdvinodjmd/HCR-Net}{GitHub}.


Artificial intelligence technology to manage smart contracts

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Choosing the right contract management software can increase productivity in any company. The main factors are cloud-based and the use of artificial intelligence. Contracts have a direct impact on the success of the company. In order to maintain an overview of the portfolio of contracts and the resulting rights and obligations, automated and clearly defined processes as well as clear lists and dashboards are required. This is especially true when the creation, conclusion and storage of contract documents is decentralized.


Cortical.io's AI makes bulk contract analysis faster and more accurate

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All the sessions from Transform 2021 are available on-demand now. In the past, reviewing large stacks of documents was a mind-numbing chore for junior attorneys -- a process that could literally consume months of multiple employees' lives. But innovations in artificial intelligence have enabled Cortical.io Using large quantities of documents as inputs and a semantic folding theory-based natural language understanding system to parse content, Contract Intelligence can transform structured agreements and unstructured documents into comprehensible data. The software is able to search, extract, classify, and compare data from contracts, policies, financial reports, and other documents, including the ability to understand the meanings of concepts and whole sentences -- more than just keywords, which might previously have been extracted and searchable using basic optical character recognition.


OCR - [TheJavaSea] OCR - Convert image to text

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Our OCR application allows you to perform basic OCR (Optical Character Recognition) in English and 100+ other languages. It is possible to recognize a...


Searching for ROI in Artificial Intelligence Deployments

#artificialintelligence

Anyone with any doubts about the interest in AI and its use across enterprise technologies only needs to look at the example of the Intelligent Document Processing (IDP) market and the kind of verticals that are investing in it to quash those doubts. According to the Everest Group's recently published report, Intelligent Document Processing (IDP) State of the Market Report 2021 (purchase required) the market for this segment alone is estimated at $700-750 million in 2020 and expected to grow at a rate of 55-65% over the next year. Cost impact is now the key driver for intelligent document processing adoption, closely followed by improving operational efficiency and productivity. These solutions blend AI technologies to efficiently process all types of documents and feed the output into downstream applications. Optical character recognition (OCR), computer vision, machine learning (ML) and deep learning models, and natural language processing (NLP) are the key core technologies powering IDP capabilities.


Digital Einstein Experience: Fast Text-to-Speech for Conversational AI

arXiv.org Artificial Intelligence

We describe our approach to create and deliver a custom voice for a conversational AI use-case. More specifically, we provide a voice for a Digital Einstein character, to enable human-computer interaction within the digital conversation experience. To create the voice which fits the context well, we first design a voice character and we produce the recordings which correspond to the desired speech attributes. We then model the voice. Our solution utilizes Fastspeech 2 for log-scaled mel-spectrogram prediction from phonemes and Parallel WaveGAN to generate the waveforms. The system supports a character input and gives a speech waveform at the output. We use a custom dictionary for selected words to ensure their proper pronunciation. Our proposed cloud architecture enables for fast voice delivery, making it possible to talk to the digital version of Albert Einstein in real-time.