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.
Creating machine learning projects with numerical attributes is easy. Most of the open-source data available for building ML models has numerical attributes. However, when we deal with enterprise data, the case is a bit different. Character or string data dominate the dataset in enterprises, making it hard to create a very accurate machine learning model. We have to clean messy strings, pull strings apart, and extract useful strings embedded in a text to bring it into a form that can be used in a machine learning pipeline.
South Australia Police will be using its automated number plate recognition system to track people known to be at risk of starting fires during the 2017-18 bushfire season, it has announced. Assistant Commissioner for South Australia Police Noel Bamford said the technology is being used as a mechanism for preventing bushfires across the state under Operation Nomad until May 2018. "Police monitoring includes maintaining an index of specific vehicles of interest in our Mobile Automated Number Plate recognition system," Bamford explained. The state's police had used the system last year to identify 86 persons of interest, with 15 of those arrested for fire starting and related offences. South Australia Police a year ago similarly began using facial-recognition technology to identify persons of interest and missing persons, though it did not say whether this system would be used under Operation Nomad.
An electric three-wheel scooter with a compartment for packages could one day become the preferred method for parcel delivery. With the eCommerce industry rapidly expanding, it's never been more crucial to find ways to reduce bulk as well as emissions from large trucks. The "Leo" design and prototype comes from Singapore Post Limited (SingPost) and TUMCREATE.
Cloud content management company Box has unveiled Box Skills, a framework for applying machine learning tools such as computer vision, video indexing, and sentiment analysis to stored content. Box Skills will facilitate businesses to re-imagine the business processes considered as impractical to digitise or automate or too expensive. Audio Intelligence: Uses audio files to create and index a text transcript that can be easily searched and manipulated in a variety of use cases; powered by IBM Watson technology. Video Intelligence: Provides transcription, topic detection and detects people to allow users to quickly look up the information they need in a video; powered by Microsoft Cognitive Services. Image Intelligence: Detects individual objects and concepts in image files, captures text through optical character recognition (OCR), and automatically adds keyword labels to images to easily build metadata on image catalogues; powered by Google Cloud Platform.
How can we make dividing bills easier when visiting a restaurant with friends or family? It makes any calculator obsolete and there is also no need anymore to ask the waiter to split the bill for you at the cash register. With our app you can split any bill, not just from restaurants alone. So go ahead and have a go with our app when you organise your next party, have a lunch with colleagues, visit a fancy restaurant with some of your best friends, or ...
FARMERS can now zap their crops with a handheld scanner to instantly determine nutritional content, which could prove crucial in mitigating the effects of climate change on food quality. "Real-time results mean farmers can add fertilisers or tweak moisture levels as crops grow" Farmers can use the app to assess the impact of changing conditions, such as extreme weather and soil quality, on the quality of their crops from year to year. It could allow farmers to mitigate the negative effects of climate change early by adding fertilisers or tweaking moisture levels as crops grow. Other companies are developing similar gadgets for consumers, and sensors that can be fitted onto a smartphone.
Where these IoT devices are in fact already doing some limited analytics at or very near the point of capture (as in the case with true Edge Computing systems), there is opportunity to create a more intelligent, more relevant, and more positive experience or outcome from the Internet of Things by using Haven OnDemand Machine Learning APIs to perform early analytics and computing that enhances or augments the data that is being acquired and aggregated at the edge. It achieved this by analyzing local law enforcement open data crime statistics to detect specific crime trends and specific crime anomalies. A more intelligent IoT solution would analyze still images to detect the presence of faces, recognize and extract text via Optical Character Recognition (OCR), identify corporate logos and even read barcodes. Examples include counting customers, analyzing customer demographics, analyzing customer personal effects to detect logos and determine brand preferences, analyzing real-time social media check-in mentions for sentiment, and point-of-sale data trend analysis.
Microsoft has released Seeing AI -- a smartphone app that uses computer vision to describe the world for the visually impaired. With the app downloaded, the users can point their phone's camera at a person and it'll say who they are and how they're feeling. It also reads and scan documents, and recognizes US currency. He points out the app doesn't just perform the basic task of optical character recognition technology, but also directs the user -- telling them to move the camera left or right to get the target in shot.
Bank of America Merrill Lynch has launched a new fintech solution that brings together artificial intelligence, machine learning and optical character recognition to help companies match incoming payments with invoices. Using AI and other new technologies, Intelligent Receivables can help these companies improve their straight through reconciliation (STR) of incoming payments and post their receivables faster. According to Rodney Gardner, the bank's head of receivables in global transaction services, the solution sets "a new bar in accounts receivable reconciliation and payment matching" and the bank ultimately hopes it can help companies reduce costs, decrease days sales outstanding and improve cash forecasting and their end-customer experience. Hilani Kerr, Bank of America Merrill Lynch's head of North America corporate global transaction services adds that the bank is looking to work with more fintech companies going forward, "to bring more innovations like Intelligent Receivables to our clients, and create practical applications of new technology that will help them achieve greater efficiency and cost savings".
Trade finance giants HSBC is working with IBM to develop a cognitive intelligence solution combining optical character recognition with advanced robotics to make global trade safer and more efficient for thousands of businesses. HSBC's Global Trade and Receivables Finance (GTRF) team facilitates over $500bn of documentary trade for customers every year, and in doing so must manually review and process up to 100m pages of documents, ranging from invoices to packing lists and insurance certificates. The new solution uses IBM's analytics technology, including intelligent segmentation and text analytics, to identify, digitise and extract key data within these documents before feeding it into the bank's transaction processing systems; boosting accuracy whilst freeing up staff for more value-adding activities, said a statement. Natalie Blyth, HSBC's Global Head of GTRF, said: "The average trade transaction requires 65 data fields to be extracted from 15 different documents, with 40 pages to be reviewed.