![]() # image for processing, the other to retrieve the text found in the image. # Extracting text requires two API calls: One call to submit the ![]() Text_recognition_url, headers=headers, json=data) # Set image_url to the URL of an image that you want to recognize. Text_recognition_url = endpoint + "/vision/v3.0/read/analyze" See the full Read OCR REST API QuickStart in Python for the following code snippets. The Operation-Location value is a URL that contains the Operation ID to be used in the next step. The call returns with a response header field called Operation-Location. Read operationĬall the Read operation to extract the text. That starts an asynchronous process that you poll with the Get Read Results operation. You use the Read operation to submit your image or document. You integrate with the service or the containers with a simple API that’s described next.Īt its core, the OCR process breaks it down into two operations. The service extracts the text and converts it into a structured JSON response that includes the extracted text lines and words with their bounding boxes and confidence scores. The following figure illustrates the high-level flow of the OCR process. Customers use it in diverse scenarios on the cloud and within their networks to solve the challenges listed in the previous section. Microsoft’s Computer Vision OCR (Read) capability is available as a Cognitive Services Cloud API and as Docker containers. To combat this challenge, you should use a technology that seamlessly handles both styles of text in the same document. Handwritten textįinally, your documents and forms have both print and handwritten text. These documents most likely contain text in multiple languages that are impossible to identify as you are scanning them at scale. ![]() Your customers and users are global so your OCR should also support international languages and locales. ![]() If you are a business that serves customers in healthcare, insurance, banking, or other verticals with additional data privacy and security requirements, you typically require not just secure online access but the flexibility to deploy within your network to ensure that the personal data does not leave your network. To extract text with high accuracy from the diverse content types, formats, and mediums, your OCR should be of the highest out-of-the-box quality, work on a variety of content textures, fonts, and styles, and be easy to integrate by using cloud APIs and SDKs. However, there are several challenges to successfully implementing OCR at scale. There are several OCR technology providers that provide this capability as services, tools, and solutions, both in the cloud and for deployment within your environment. Optical Character Recognition (OCR) is the foundational technology that drives the digitization of content today by extracting text from images, documents, and screens. These insights power knowledge mining, business process automation, and accessibility for everyone regardless of the source of content, location of users, and the language and medium of communication. Businesses today are racing to convert their scanned paper documents, digital files, and even on-screen content into actionable insights. ![]()
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