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DLR – for document layer recognition [citation needed] These basic technologies allow extracting information from paper documents for further processing in the enterprise information systems such as ERP, CRM, and others. [citation needed] The documents for data capture can be divided into 3 groups: structured, semi-structured, and unstructured.
Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network , and temporal data using a recurrent neural network .
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Before the rise of payment apps and digital wallets, wire transfers were a go-to payment method for customers who needed to send money without handing over cash, and they're still a valid option...
One is called Story Bridge, which uses AI and generative AI to assess performance data and identify the root cause of variances or deviations and then generates summaries to enable decision-making.
AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces.
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