Reading huge documents can be very tiring and very time taking. You must have seen many software or applications where you just click a picture and get key information from the document. This is done by a technique called Optical Character Recognition (OCR). Optical Character Recognition is one of the key researches in the field of AI in recent years. Optical Character Recognition is the process of recognizing text from an image by understanding and analyzing its underlying patterns. This blog post will focus on implementing and comparing various OCR algorithms provided by PaddleOCR using just a few lines of code. Optical Character Recognition is the technique that recognizes and converts text into a machine-readable format by analyzing and understanding its underlying patterns. OCR can recognize handwritten text, printed text and texts "in the wild". In short, OCR enables computers to read. But how does OCR work? OCR makes use of Deep learning and computer vision techniques.

A robust OCR process can convert client documents into structured data in a digestible format that can be analyzed for client cross-selling, up-selling, or new business opportunities. OCR programs can assist sales and underwriting teams by automatically extracting and transforming key details from RFPs and lengthy policy documents. OCR enables insurance sales professionals to streamline and drive efficiencies by automatically scrubbing RFP emails, multiple PDF documents, plan booklets, and even scanned images of policy documents for key details that can be transformed into a format appropriate for processing. This data can then be loaded into the insurance company's sales and underwriting systems, like a quoting and rating engine, creating an initial shell quote in seconds. Additionally, many insurance companies still maintain vast quantities of historical data in unstructured and paper formats.

Voice artificial intelligence is an emerging technology that uses voice commands to interact with humans. The technology is witnessing tremendous growth and intense research in modern engineering to explore untapped areas. We are well accustomed to hearing AI voices narrating monotone articles and reports. One of the most trending examples of their use by many people is Alexa and Siri-enabled devices. These devices are getting significant recognition, and the market for similar products is growing exceptionally.

Today, large enterprises are grappling with an onslaught of unstructured data and documents. IDC and Seagate predict that the global data sphere will grow to 163 zettabytes by 2025, and about 80 percent of that will be unstructured. In regulated industries, such as financial services, the challenges posed by unstructured and semi-structured data are exponentially higher. Traditional methods–ranging from manual entry to Optical Character Recognition (OCR)–have proved woefully inadequate. Even more recent and highly heralded methods such as Robotic Process Automation (RPA) have proven to be piecemeal solutions to the challenge.

Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce.

Feitian Technologies showed off its newest portfolio of four Android handheld devices, three of which include fingerprint biometrics, and which support an assortment of applications from law enforcement to voting. The Handheld Biometric Identification Terminal (V11) is a wireless, five-inch terminal with fingerprint, iris, and face biometric verification. Customers can choose between fingerprint sensors certified for single flat fingers at FAP30, FAP20, or FAP10, from Integrated Biometrics, Suprema, Idemia, Futronic, Aratek and SecuGen, according to the product page. The device also supports scanning of digital identity documents through NFC, MRZ Passport reading, and optical character recognition (OCR). The Multifunction Handheld Terminal (V12) terminal is intended for law enforcement that sports a fingerprint sensor with live finger detection, breathalyzer, and narcotics detector, and can issue tickets.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Before Dr. Alan Turing designed the first computer, people merely dreamed of intelligent machines that could read paperwork and do most of their grunge work for them. Science-fiction movies depict advanced software processing large amounts of documents to find hidden insights that save the day. Today this is available in real life from progressive-thinking software providers. One of them, San Francisco-based Automation Hero, today launched v6.0 of its Hero Platform, a SaaS service the company claims takes a quantum leap in OCR (optical character recognition) document-processing accuracy.

A significant enhancement has been made to PaddleOCR, the multilingual optical character recognition (OCR) toolkits. With over 80 different multi-language recognition models and an easy-to-use interface, PaddleOCR is an open-source OCR repository worth checking out. OCRv3 PP-OCRv3 has a 5% to 11% increase in accuracy in English and multilingual scenarios. Annotation functions for tables, irregular text pictures, and essential information extraction tasks have been added to PPOCRLabelv2. "Dive into OCR," a new interactive e-book, is now available.

The US Postal Service is facing more than just stern warnings over its decision to buy mostly gas-powered mail delivery trucks. Environmental activist groups (including the Center for Biological Diversity and the Sierra Club) and 16 states have filed lawsuits in California and New York State to challenge the Postal Service's Next Generation Delivery Vehicle purchasing decision. They argue the USPS's environmental review was flawed and illegal, ignoring the "decades of pollution" the combustion-engine trucks would produce. The USPS allegedly violated the National Environmental Policy Act by committing to buy 165,000 delivery vehicles (just 10 percent of them electric) without first conducting a "lawful" environmental review. The service only started its review six months after it had signed a contract, according to the California lawsuit.

Optical Character Recognition (OCR) with less than 10 Lines of Code using Python · Want to read more stories like this? It costs only 4,16$ per month. Within the area of Computer Vision is the sub-area of Optical Character Recognition (OCR), which aims to transform images into texts. OCR can be described as converting images containing typed, handwritten or printed text into characters that a machine can understand. It is possible to convert scanned or photographed documents into texts that can be edited in any tool, such as the Microsoft Word.