post https://app.nanonets.com/api/v2/OCR/Model//LabelFile
Nanonets API to upload files to your OCR model in async mode and to make predictions based on the uploaded images or documents using the file path hosted locally on your system. You can specify multiple file path, ideal for larger files with more than 3 pages.
You can test this API on this page using the API key. First, generate the API key, upload the file stored locally on your system, set async = true, and enter the model_id in the parameter boxes below. Once you have added all the parameters, hit the “Try It!” button on the right side panel to see the response in the response box on the right side panel.
Attributes:
message:This indicates the overall success of the API call.result:A list of objects representing each page of the processed files. These objects include the following attributes:message:This indicates the success of page processed in the file.input:This is the name of the file uploaded to the model using this API.prediction:An empty list, meaning no predictions were made. In the case of async precessing prediction list is empty because in the async processing the file is processed at a later point in time.page:The page number in the document where the label is located, with 0 representing the first page and so on.request_file_id:The unique identifier of the file you uploaded to the model for prediction. You can find this ID on the extract data page of the model for each file.id:This is the unique identifier for the prediction.request_metadata:This is a body parameter that you include in the request when making an API call. It is returned in the API response and is typically used to uniquely identify and map the file you uploaded.processing_type:This indicates how the file was processed, specifying whether it was handled async or sync. If this field in the response is empty, it means the file was processed sync. If it shows "async," the file was processed async. For more details on sync and async processing, please refer to this page.size:This represent the dimension of the pages processed.
signed_urls:An object containing signed URLs for accessing various processed files