Optical Character Recognition

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.

Bank of America Merrill Lynch (BAML) is launching a new solution – intelligent receivables – that uses artificial intelligence (AI) and other software to help companies "vastly improve" their straight-through reconciliation (STR) of incoming payments to help them post their receivables faster, reports Banking Technology's sister publication Paybefore. The service is "ideally suited" for companies that manage a large volume of payments where the remittance information is either missing or received separately from the payment. Incomplete remittance information typically leads to an arduous and costly reconciliation process, says Rodney Gardner, head of global receivables in global transaction services at BAML. "Our solution brings together AI, machine learning and optical character recognition, setting a new bar in accounts receivable reconciliation and payment matching," adds Gardner.

Bangalore: HSBC, the world's leading trade finance bank, 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 $500 billion of documentary trade for customers every year, and in doing so must manually review and process up to 100 million pages of documents, ranging from invoices to packing lists and insurance certificates. The new solution uses IBM's advanced 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. "The average trade transaction requires 65 data fields to be extracted from 15 different documents, with 40 pages to be reviewed," said Natalie Blyth, HSBC's Global Head of GTRF.

HSBC is using IBM artificial intelligence (AI) technology to process documents related to international trade. Currently around 100 million pages of documents, such as invoices and insurance documents, are manually reviewed and processed by HSBC staff. Using optical character recognition and robotics technology from IBM, HSBC's Global Trade and Receivables Finance (GTRF) is automating the review of documents and sending them automatically to the bank's transaction processing systems. For example, a report from financial services management consultancy Opimas predicted that in 2017, discounting acquisitions of startups, finance firms in the investment sector would spend $1.5bn on robotic process automation, machine learning, deep learning and cognitive analytics, increasing by 75% to $2.8bn in 2021.

Postal Service (USPS) crates sit on the floor at the Brookland Post Office in Washington, D.C., U.S. No customer data was stolen in a recent data breach, USPS officials say. Postal Service is warning that it will likely default on up to $6.9 billion in payments for future retiree health benefits for the fifth straight year. It is citing a coming cash crunch that could disrupt day-to-day mail delivery. Postmaster General Megan Brennan stressed an urgent need for federal regulators to grant the Postal Service wide freedom to increase stamp prices to cover costs.

Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging.