Now annotate each piece of text or data with an appropriate field or label. The accuracy of the OCR model you build will largely depend on the quality and quantity of the files/images uploaded at this stage Step 3: Annotate text on the files/images Upload sample files that will be used to train the OCR models. Login to Nanonets and click on “Create your own OCR model”. Watch the video below to follow the first 4 steps in this method: How to Train your own OCR Model with Nanonets Step 1: Create your own OCR model You can typically build, train and deploy a model for any image or document type, in any language, all in under 25 minutes (depending on the number of files used to train the model). The extracted data can be displayed in a “List View” or “JSON” format.Įxtract text from image by building a custom Nanonets OCR modelīuilding a custom OCR model with Nanonets is easy. Nanonets is not bound by the template of the image. You can even choose to edit/correct the field values and labels at this stage. You can easily double-check whether the text has been correctly recognized and matched with an appropriate field or tag. Quickly verify the text extracted from each file, by checking the table view on the right. Step 3: TestĪllow a few seconds for the model to run and extract text from the image. Step 2: Add filesĪdd the files/images from which you want to extract text. If none of the pre-trained OCR models suit your requirements, you can skip ahead to find out how to create a custom OCR model. Login to Nanonets and select an OCR model that is appropriate to the image from which you want to extract text and data. Nanonets extracting text from images of receipts Step 1: Select an appropriate OCR model
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