Optical Character Recognition (OCR) Software Market Analysis Report2023-2029
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Apr 05, 2023 (The Expresswire) -- [120 Insights] "Optical Character Recognition (OCR) Software Market" Size 2023 Key players Profiled in the Report are [, ABBY Software, Google, CCi Intelligence, Captricity, Exper-OCR, Nuance Communications, LEAD Technologies, Creaceed, IBM, Anyline, Microsoft, ATAPY Software, Adobe Systems,] most important, influential, or successful companies, brands, or individuals within a Optical Character Recognition (OCR) Software market 2023 to 2029.
The Global Optical Character Recognition (OCR) Software market is anticipated to rise at a considerable rate during the forecast period. The market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon. Get a Sample Copy of the Optical Character Recognition (OCR) Software Report 2023
Optical Character Recognition (OCR) Software Market -SegmentationAnalysis:
Report further studies the market development status and future Optical Character Recognition (OCR) Software Market trend across the world. Also, it splits Optical Character Recognition (OCR) Software market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.
Segment by Type
● Desktop based OCR ● Mobile based OCR ● Cloud based OCR ● OtherWhich growth factors drives the Optical Character Recognition (OCR) Software market growth?
Increasing use of is expected to drive the growth of the Optical Character Recognition (OCR) Software Market.
Segment by Application
● IT and Telecom ● Media and Entertainment ● OthersGet a Sample Copy of the Report at https://www.360marketupdates.Com/enquiry/request-sample/20376936
Customer requirement: -
Yes. Providing a complete overview of the global Optical Character Recognition (OCR) Software market is a complex task, as there are many different markets and industries around the world. However, I can provide a high-level summary of some of the key trends and factors that are currently impacting the global Optical Character Recognition (OCR) Software market. Economic Growth, Technology, E-commerce, Globalization, Sustainability, Demographics, and Political and regulatory risks are just a few of the many factors that are currently shaping the global market. It is a dynamic and ever-changing environment, and businesses that are able to adapt to new trends and challenges are likely to be the most successful.
Yes. It is true that the global Optical Character Recognition (OCR) Software market provides a wealth of high-quality data for businesses and investors to analyze and make informed decisions. There are many different sources of market data, including government statistics, industry reports, financial news, and market research firms. Some of the key types of data that are available from the global Optical Character Recognition (OCR) Software market include, Economic data, Financial data, Industry data, and Consumer data However, it is important to carefully evaluate the quality and reliability of data sources and to use multiple sources of data to gain a more complete understanding of the Optical Character Recognition (OCR) Software market.
Yes. For the United States, Canada, Mexico, Germany, France, United Kingdom, Russia, Italy, China, Japan, Korea, India, Southeast Asia, Australia, Brazil, Saudi Arabia, etc. It also throws light on the progress of key regional Optical Character Recognition (OCR) Software Markets such as North America, Europe, Asia-Pacific, South America, and Middle East, and Africa
Get a Sample PDF of report https://www.360marketupdates.Com/enquiry/request-sample/20376936
Yes. Optical Character Recognition (OCR) Software Market analysis is the process of evaluating market conditions and trends in order to make informed business decisions. A market can refer to a specific geographic location, particular industry, or sector, and develop strategies for entering or expanding in a particular Optical Character Recognition (OCR) Software market.
Optical Character Recognition (OCR) Software Market analysis can also involve forecasting future market trends and conditions, based on factors like technological change, regulatory developments, or demographic shifts. This can be used to develop long-term strategic plans and to identify potential risks and opportunities for growth.
Industry Brief:
Optical Character Recognition (OCR) Software Marketsize, segment (mainly coveringMajorType (, Desktop based OCR, Mobile based OCR, Cloud based OCR, Other, ,),End Users (, IT and Telecom, Media and Entertainment, Others,), and regions), recent status, development trendsa and competitor landscape. Furthermore, the 120 pages report provides detailed cost analysis, supply chain.
Technological innovation and advancement will further optimize the performance of the product, making it more widely used in downstream end users. Also, Consumer behaviour analysis and market dynamics (drivers, restraints, opportunities) provides crucial information for knowing the Optical Character Recognition (OCR) Software market.
DMarket segment by Region/Country including: -
● North America (United States, Canada, and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Spain, etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Southeast Asia, etc.) ● South America (Brazil, Argentina, Colombia, etc.) ● Middle East and Africa (South Africa, UAE, Saudi Arabia, etc.)User center of Optical Character Recognition (OCR) Software market 2023
Yes. As the COVID-19 and the Russia-Ukraine war are profoundly affecting the global supply chain relationship and raw material price system, we have definitely taken them into consideration throughout the research, and we elaborate at full length on the impact of the pandemic and the war on the Precious Metals Industry.
Final Report will add the analysis of the impact of COVID-19 on this industry.
TO UNDERSTAND HOW COVID-19 IMPACT IS COVERED IN THIS REPORT - REQUEST SAMPLE
Which market dynamics affect the business?
The report provides a detailed evaluation of the market by highlighting information on different aspects which include drivers, restraints, opportunities, and threats. This information can help stakeholders to make appropriate decisions before investing.
It also provides accurate information and cutting-edge analysis that is necessary to formulate an ideal business plan, and to define the right path for rapid growth for all involved industry players. With this information, stakeholders will be more capable of developing new strategies, which focus on market opportunities that will benefit them, making their business endeavors profitable in the process.
Optical Character Recognition (OCR) Software Market - Competitive Analysis:
With the aim of clearly revealing the competitive situation of the industry, we concretely analyze not only the leading enterprises that have a voice on a global scale, but also the regional small and medium-sized companies that play key roles and have plenty of potential growth.Please find the key player list in Summary.
Optical Character Recognition (OCR) Software Industry leading players are the ones that have the biggest impact, the most market shares 2023, the best reputation, or the highest revenue within their field they are
Who are the Leading Players in Optical Character Recognition (OCR) Software Market?
● ABBY Software ● Google ● CCi Intelligence ● Captricity ● Exper-OCR ● Nuance Communications ● LEAD Technologies ● Creaceed ● IBM ● Anyline ● Microsoft ● ATAPY Software ● Adobe SystemsGet a Sample Copy of the Report at
https://www.360marketupdates.Com/enquiry/request-sample/20376936
Both Primary and Secondary data sources are being used while compiling the report.Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users.Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.
Please find a more complete list of data sources in Chapters:
1.To study and analyze the global Optical Character Recognition (OCR) Software consumption (value) by key regions/countries, product type and application
2.To understand the structure of Optical Character Recognition (OCR) Software Market by identifying its various sub segments.
3.Focuses on the key global Optical Character Recognition (OCR) Software manufacturers, to define, describe and analyze the value, market share, market competition landscape, Porter's five forces analysis, SWOT analysis and development plans in next few years.
4.To analyze the Optical Character Recognition (OCR) Software with respect to individual growth trends, future prospects, and their contribution to the total market.
5.To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
6.To project the consumption of Optical Character Recognition (OCR) Software submarkets, with respect to key regions (along with their respective key countries).
7.To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.
8.To strategically profile the key players and comprehensively analyze their growth strategies.
Get a Sample Copy of the Report at https://www.360marketupdates.Com/enquiry/request-sample/20376936
Major Points from Table of Contents
1 Market Overview
1.1 Optical Character Recognition (OCR) Software Introduction
1.2 Market Analysis by Type
1.3 Market Analysis by Applications
1.4 Market Analysis by Regions
1.5 Market Dynamics
2 Manufacturers Profiles
3 Global Optical Character Recognition (OCR) Software Market Competition, by Manufacturer
4 Global Optical Character Recognition (OCR) Software Market Analysis by Regions
5 North America Optical Character Recognition (OCR) Software by Countries
6 Europe Optical Character Recognition (OCR) Software by Countries
7 Asia-Pacific Optical Character Recognition (OCR) Software by Countries
8 Latin America, Middle and Africa Optical Character Recognition (OCR) Software by Countries
9 Optical Character Recognition (OCR) Software Market Segment by Type
10 Optical Character Recognition (OCR) Software Market Segment by Application
11 Optical Character Recognition (OCR) Software Market Forecast (2016-2021)
12 Sales Channel, Distributors, Traders and Dealers
13 Appendix
13.1 Methodology
13.2 Data Source
And more…
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● To gain insightful analyses of the market and have comprehensive understanding of the global Optical Character Recognition (OCR) Software Market and its commercial landscape. ● Assess the production processes, major issues, and solutions to mitigate the development risk. ● To understand the most affecting driving and restraining forces in the Optical Character Recognition (OCR) Software Market and its impact in the global market. ● Learn about the Optical Character Recognition (OCR) Software Market strategies that are being adopted by leading respective organizations. ● To understand the future outlook and prospects for the Optical Character Recognition (OCR) Software Market. ● Besides the standard structure reports, we also provide custom research according to specific requirementsPurchase this Report (Price 2980 USD for a Single-User License) https://www.360marketupdates.Com/purchase/20376936
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Artificial Intelligence: Africa Takes Up The Subject
(MENAFN- African Press Organization)download logoAs AI systems like ChatGPT begin to disrupt our daily lives, the global race for innovation is accelerating. The biggest advances are taking place in the United States, China and Europe. Yet Africa is gearing up to take its place and embrace the fourth industrial revolution. An illustration from Dakar with BAAMTU, a company specialized in software engineering, big data and artificial intelligence.
A lever for progress and growth
Developed by OpenAI in San Francisco, the chatbotChatGPT is on everyone's lips. Trained on a vast collection of texts, ChatGPT can carry on human-like conversations, answer questions, produce computer code and generate natural-sounding prose on any subject.
This impressive artificial intelligence has caught the interest of companies and projects around the world. That includes Africa, which also wants to take advantage of the opportunities presented by AI.
In Senegal, a growing number of initiatives are developing new applications.
"African languages are poorly represented on the web. With chatbots, we provide custom conversational systems in any language. We are thus improving the means of translation and facilitating access to information for all," said Mayoro Diagne, Director of Operations atbaamtuin Dakar. His company is tackling other problems specific to Africa, using artificial intelligence.
"In the field of telemedicine, chatbots help guide people through their care. At the request of the Ministry of Health, we have developed a self-diagnosis technology for hemophilia, accessible on a smartphone," said Derguene Mbaye, a research engineer at BAAMTU.
The Dakar-based company is also using artificial intelligence to make mundane admin tasks easier:
"With optical character or image recognition, artificial intelligence can easily compile data and automate tasks. It is a great alternative to paper, when many citizens have difficulties finding their civil status," Mbaye added.
African expertise on the international stage
Agriculture, education, security, health, commerce: artificial intelligence is gradually penetrating all spheres of daily life.
"New jobs are coming up. We are seeing new training institutions emerge in Senegal, while more and more local companies are interested in data science and want to take advantage of artificial intelligence," Diagne said,"Africa is in the process of taking a leap forward and building a proper model to meet its own challenges, in line with the expectations of its people."
This enthusiasm is raising a lot of interest and questions, which require more discussion by a broad range of people. With the NTF V project at the International Trade Centre (ITC), BAAMTU in February held a conference at the International Trade Fair for Digital Economy Professionals in Dakar.
It was an opportunity to showcase its expertise in artificial intelligence and to present its chatbot. Since this presentation, BAAMTU has been approached by new partners and continues to expand through its subsidiaries, particularly in Nigeria.
"BAAMTU's goal is to embrace the opportunities of artificial intelligence to ensure that it has a positive human impact. Just like the Tunisian startupinstadeep , which has been purchased by the German laboratorybiontech , we are convinced that African expertise in artificial intelligence has strong assets to promote internationally," Mbaye said.
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New Artificial Intelligence Does Something Extraordinary — It Remembers
When you return to school after summer break, it may feel like you forgot everything you learned the year before. But if you learned like an AI system does, you actually would have — as you sat down for your first day of class, your brain would take that as a cue to wipe the slate clean and start from scratch.
AI systems' tendency to forget the things it previously learned upon taking on new information is called catastrophic forgetting.
That's a big problem. See, cutting-edge algorithms learn, so to speak, after analyzing countless examples of what they're expected to do. A facial recognition AI system, for instance, will analyze thousands of photos of people's faces, likely photos that have been manually annotated, so that it will be able to detect a face when it pops up in a video feed. But because these AI systems don't actually comprehend the underlying logic of what they do, teaching them to do anything else, even if it's pretty similar — like, say, recognizing specific emotions — means training them all over again from scratch. Once an algorithm is trained, it's done, we can't update it anymore.
For years, scientists have been trying to figure out how to work around the problem. If they succeed, AI systems would be able to learn from a new set of training data without overwriting most of what they already knew in the process. Basically, if the robots should someday rise up, our new overlords would be able to conquer all life on Earth and chew bubblegum at the same time.
But still, catastrophic forgetting is one of the major hurdles preventing scientists from building an artificial general intelligence (AGI) — AI that's all-encompassing, empathetic, and imaginative, like the ones we see in TV and movies.
In fact, a number of AI experts who attended The Joint Multi-Conference on Human-Level Artificial Intelligence last week in Prague said, in private interviews with Futurism or during panels and presentations, that the problem of catastrophic forgetting is one of the top reasons they don't expect to see AGI or human-level AI anytime soon.
Catastrophic forgetting is one of the top reasons experts don't expect to see human-level AI anytime soon.
But Irina Higgins, a senior research scientist at Google DeepMind, used her presentation during the conference to announce that her team had begun to crack the code.
She had developed an AI agent — sort of like a video game character controlled by an AI algorithm — that could think more creatively than a typical algorithm. It could "imagine" what the things it encountered in one virtual environment might look like elsewhere. In other words, the neural net was able to disentangle certain objects that it encountered in a simulated environment from the environment itself.
This isn't the same as a human's imagination, where we can come up with new mental images altogether (think of a bird — you can probably conjure up an image of what a fictional spherical, red bird might look like in your mind's eye.) The AI system isn't that sophisticated, but it can imagine objects that it's already seen in new configurations or locations.
"We want a machine to learn safe common sense in its exploration so it's not damaging itself," said Higgins in her speech at the conference, which had been organized by GoodAI. She had published her paper on the preprint server arXiv earlier that week, describing work that allows previously-developed AI agents to continuously learn without forgetting earlier training.
Let's say you're walking through the desert (as one does) and you come across a cactus. One of those big, two-armed ones you see in all the cartoons. You can recognize that this is a cactus because you have probably encountered one before. Maybe your office bought some succulents to liven up the place. But even if your office is cactus-free, you could probably imagine what this desert cactus would look like in a big clay pot, maybe next to Brenda from accounting's desk.
Now Higgins' AI system can do pretty much the same thing. With just five examples of how a given object looks from various angles, the AI agent learns what it is, how it relates to the environment, and also how it might look from other angles it hasn't seen or in different lighting. The paper highlights how the algorithm was trained to spot a white suitcase or an armchair. After its training, the algorithm can then imagine how that object would look in an entirely new virtual world and recognize the object when it encounters it there.
"We run the exact setup that I used to motivate this model, and then we present an image from one environment and ask the model to imagine what it would look like in a different environment," Higgins said. Again and again, her new algorithm excelled at the task compared to AI systems with entangled representations, which could predict fewer qualities and characteristics of the objects.
Image Credit: Emily ChoIn short, the algorithm is able to note differences between what it encounters and what it has seen in the past. Like most people but unlike most other algorithms, the new system Higgins built for Google can understand that it hasn't come across a brand new object just because it's seeing something from a new angle. It can then use some spare computational power to take in that new information; the AI system updates what it knows about the world without needing to be retrained and re-learn everything all over again. Basically, the system is able to transfer and apply its existing knowledge to the new environment. The end result is a sort of spectrum or continuum showing how it understands various qualities of an object.
Higgins' model alone won't get us to AGI, of course. But it marks an important first step towards AI algorithms that can continuously update as they go, learning new things about the world without losing what they already had.
"I think it's very crucial to reach anything close to artificial general intelligence," Higgins said.
"I think it's very crucial to reach anything close to artificial general intelligence."
And this work is all still in its early stages. These algorithms, like many other object recognition AI tools, excel at a rather narrow task with a constrained set of rules, such as looking at a photo and picking out a face among many things that are not faces. But Higgins' new AI system is doing a narrow task in such a way that more closely resembles creativity and some digital simulation of an imagination.
And even though Higgins' research didn't immediately bring about the era of artificial general intelligence, her new algorithm already has the ability to improve the existing AI systems we use all the time. For instance, Higgins tried her new AI system on a major set of data used to train facial recognition software. After analyzing the thousands and thousands of headshots found in the dataset, the algorithm could create a spectrum of any quality with which those photos have been labeled. As an example, Higgins presented the spectrum of faces ranked by skin tone.
Higgins then revealed that her algorithm was able to do the same for the subjective qualities that also find their ways into these datasets, ultimately teaching human biases to facial recognition AI. Higgins showed how images that people had labeled as "attractive" created a spectrum that pointed straight towards the photos of young, pale women. That means any AI system that had been trained with these photos — and there are many of them out there — now hold the same racist views as do the people who labeled the photos in the first place: that white people are more attractive.
This creative new algorithm is already better than we are when it comes to finding new ways to detect human biases in other algorithms so engineers can go in and remove them.
So while it can't replace artists quite yet, Higgins' team's work is a pretty big step towards getting AI to imagine more like a human and less like an algorithm.
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