Yann Andre LeCun, a French computer scientist who focuses on machine learning, computer vision, mobile robotics, and computational neuroscience, recently tweeted that one of his articles has been rejected from NeurIPS 2021. Yann LeCun is a Silver Professor at New York University's Courant Institute of Mathematical Sciences and Vice President, Chief AI Scientist at Facebook. He is well-known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs) and is often regarded as the inventor of convolutional nets. He is also a co-creator of the DjVu image compression technology. The author is a multifaceted individual with academic and industrial experience in artificial intelligence, machine learning, deep learning, computer vision, intelligent data analysis, data mining, data compression, digital library systems, and robotics.

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. In this code pattern, learn how to use optical character recognition (OCR) and the IBM Maximo Visual Inspection object recognition service to identify and read license plates. Using IBM Maximo Visual Inspection and the Custom Inference Scripts, you can build an object detection model to identify license plates from images of cars. The models in the IBM Maximo Visual Inspection object recognition service can identify portions of images that represent a license plate. Then, the post custom inference script can crop this area and use open source to perform OCR on the text to return the license plate.

Yeelen Knegtering, CEO & Co-founder of Klippa, is passionate about developing digital products that help people to save time on administrative hassle and spend time on the things they love. With a degree in Information Technology at the University of Groningen, he started Klippa with the idea that there had to be a better way to organize and manage receipts. Now, Klippa is a document digitization company with a focus on digitizing and automating document streams for companies.

The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both processes introduce error in texts, so it is difficult to use the data for other purposes than just to read it, i.e. the processing of those texts by other applications like e-learning, learning of languages, electronic tutorials, data minning, information retrieval and even more specialized systems such as tiflologic software, specifically blinded people-oriented applications like automatic reading, where the text would be error free as possible in order to make easier the text to speech task, and so on. In this paper it is showed an application of automatic recognition and correction of ortographic errors in electronic texts. This task is composed of three stages: a) error detection; b) candidate corrections generation; and c) correction -selection of the best candidate. The proposal is based in part of speech text categorization, word similarity, word diccionaries, statistical measures, morphologic analisys and n-grams based language model of Spanish.

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. Included among the other applications that may come to mind is OCR (Optical Character Recognition) software. OCR software must read handwritten text, or pages of printed books, for general electronic documents in which each character is well defined.

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.

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

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}.

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.

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.