fromgoogle.cloudimportvisionimportgoogle.authfromgoogle.auth.transportimportrequestsfromgoogle.api_core.client_optionsimportClientOptionsaudience="https://ENDPOINT:443"api_endpoint="ENDPOINT:443"defvision_client(creds):opts=ClientOptions(api_endpoint=api_endpoint)returnvision.ImageAnnotatorClient(credentials=creds,client_options=opts)defmain():creds=Nonetry:creds,project_id=google.auth.default()creds=creds.with_gdch_audience(audience)req=requests.Request()creds.refresh(req)print("Got token: ")print(creds.token)exceptExceptionase:print("Caught exception"+str(e))raiseereturncredsdefvision_func(creds):vc=vision_client(creds)input_config={"content":"BASE64_ENCODED_FILE"}features=[{"type_":vision.Feature.Type.FEATURE_TYPE}]# Each requests element corresponds to a single file. To annotate more# files, create a request element for each file and add it to# the array of requestsreq={"input_config":input_config,"features":features}metadata=[("x-goog-user-project","projects/PROJECT_ID")]resp=vc.annotate_file(req,metadata=metadata)print(resp)if__name__=="__main__":creds=main()vision_func(creds)
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[],[],null,["# Detect text in files\n\nThis page shows you how to detect text in files using the\nOptical Character Recognition (OCR) API on Google Distributed Cloud (GDC) air-gapped appliance.\n\nThe OCR service of Vertex AI on\nGDC air-gapped appliance detects text in PDF and TIFF files using the\n`BatchAnnotateFiles` API method.\n| **Note:** The `BatchAnnotateFiles` API method only supports a single request per batch call.\n\nBefore you begin\n----------------\n\nBefore you can start using the OCR API, you must have a project\nwith the OCR API enabled and have the appropriate credentials.\nYou can also install client libraries to help you make calls to the API. For\nmore information, see [Set up a character recognition project](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vai-set-up-ocr).\n\nDetect text with inline requests\n--------------------------------\n\nThe `BatchAnnotateFiles` method detects text from a batch of PDF or TIFF files.\nYou send the file from which you want to detect text directly as content in the\nAPI request. The system returns the resulting detected text in JSON format in\nthe API response.\n\nYou must specify values for the fields in the JSON body of your API request. The\nfollowing table contains a description of the request body fields you must\nprovide when you use the `BatchAnnotateFiles` API method for your text\ndetection requests:\n\n### Make an inline API request\n\nMake a request to the OCR pre-trained API using the REST API\nmethod. Otherwise, interact with the OCR pre-trained API from a\nPython script to detect text from PDF or TIFF files.\n| **Note:** The `BatchAnnotateFiles` API method only supports a single request per batch call.\n\nThe following examples show how to detect text in a file using\nOCR: \n\n### REST\n\nFollow these steps to detect text in files using the REST API method:\n\n1. Save the following `request.json` file for your request body:\n\n cat \u003c\u003c- EOF \u003e request.json\n {\n \"requests\": [\n {\n \"input_config\": {\n \"content\": \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-err\"\u003eBASE\u003c/span\u003e\u003cspan class=\"devsite-syntax-mi\"\u003e64\u003c/span\u003e\u003cspan class=\"devsite-syntax-err\"\u003e_ENCODED_FILE\u003c/span\u003e\u003c/var\u003e,\n \"mime_type\": \"application/pdf\"\n },\n \"features\": [\n {\n \"type\": \"\u003cvar translate=\"no\"\u003eFEATURE_TYPE\u003c/var\u003e\"\n }\n ],\n \"image_context\": {\n \"language_hints\": [\n \"\u003cvar translate=\"no\"\u003eLANGUAGE_HINT_1\u003c/var\u003e\",\n \"\u003cvar translate=\"no\"\u003eLANGUAGE_HINT_2\u003c/var\u003e\",\n ...\n ]\n },\n \"pages\": []\n }\n ]\n }\n EOF\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eBASE64_ENCODED_FILE\u003c/var\u003e: the Base64 representation (ASCII string) of your binary file content. This string begins with characters that look similar to `/9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==`.\n - \u003cvar translate=\"no\"\u003eFEATURE_TYPE\u003c/var\u003e: the type of text detection you need from the file. Allowed values are `TEXT_DETECTION` or `DOCUMENT_TEXT_DETECTION`.\n - \u003cvar translate=\"no\"\u003eLANGUAGE_HINT\u003c/var\u003e: the BCP 47 language tags to use as language hints for text detection, such as `en-t-i0-handwrit`. This field is optional and the system interprets an empty value as automatic language detection.\n2. [Get an authentication token](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-auth).\n\n3. Make the request:\n\n ### curl\n\n curl -X POST \\\n -H \"Authorization: Bearer \u003cvar translate=\"no\"\u003eTOKEN\u003c/var\u003e\" \\\n -H \"x-goog-user-project: projects/\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d @request.json \\\n https://\u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e/v1/files:annotate\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eTOKEN\u003c/var\u003e: [the authentication token](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-auth) you obtained.\n - \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: your project ID.\n - \u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e: the OCR endpoint that you use for your organization. For more information, [view service status and endpoints](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-status).\n\n ### PowerShell\n\n $headers = @{\n \"Authorization\" = \"Bearer \u003cvar translate=\"no\"\u003eTOKEN\u003c/var\u003e\"\n \"x-goog-user-project\" = \"projects/\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e\"\n }\n\n Invoke-WebRequest\n -Method POST\n -Headers $headers\n -ContentType: \"application/json; charset=utf-8\"\n -InFile request.json\n -Uri \"\u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e/v1/files:annotate\" | Select-Object -Expand Content\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eTOKEN\u003c/var\u003e: [the authentication token](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-auth) you obtained.\n - \u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e: the OCR endpoint that you use for your organization. For more information, [view service status and endpoints](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-status).\n\n### Python\n\nFollow these steps to use the OCR service from a Python\nscript to detect text in a file:\n\n1. [Install the latest version of the OCR client library](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-install-libraries).\n\n2. [Set the required environment variables on a Python script](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vai-set-up-ocr#set-env-var).\n\n3. [Authenticate your API request](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-auth).\n\n4. Add the following code to the Python script you created:\n\n from google.cloud import vision\n import google.auth\n from google.auth.transport import requests\n from google.api_core.client_options import ClientOptions\n\n audience = \"https://\u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e:443\"\n api_endpoint=\"\u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e:443\"\n\n def vision_client(creds):\n opts = ClientOptions(api_endpoint=api_endpoint)\n return vision.https://cloud.google.com/python/docs/reference/vision/latest/google.cloud.vision_v1.services.image_annotator.ImageAnnotatorClient.html(credentials=creds, client_options=opts)\n\n def main():\n creds = None\n try:\n creds, project_id = google.auth.default()\n creds = creds.with_gdch_audience(audience)\n req = requests.Request()\n creds.refresh(req)\n print(\"Got token: \")\n print(creds.token)\n except Exception as e:\n print(\"Caught exception\" + str(e))\n raise e\n return creds\n\n def vision_func(creds):\n vc = vision_client(creds)\n input_config = {\"content\": \"\u003cvar translate=\"no\"\u003eBASE64_ENCODED_FILE\u003c/var\u003e\"}\n features = [{\"type_\": vision.https://cloud.google.com/python/docs/reference/vision/latest/google.cloud.vision_v1.types.Feature.html.Type.\u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eFEATURE_TYPE\u003c/span\u003e\u003c/var\u003e}]\n # Each requests element corresponds to a single file. To annotate more\n # files, create a request element for each file and add it to\n # the array of requests\n req = {\"input_config\": input_config, \"features\": features}\n\n metadata = [(\"x-goog-user-project\", \"projects/\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e\")]\n\n resp = vc.annotate_file(req,metadata=metadata)\n\n print(resp)\n\n if __name__==\"__main__\":\n creds = main()\n vision_func(creds)\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eENDPOINT\u003c/var\u003e: the OCR endpoint that you use for your organization. For more information, [view service status and endpoints](/distributed-cloud/hosted/docs/latest/appliance/application/ao-user/vertex-ai-api-status).\n - \u003cvar translate=\"no\"\u003eBASE64_ENCODED_FILE\u003c/var\u003e: the Base64 representation (ASCII string) of your file content. This string begins with characters that look similar to `/9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==`.\n - \u003cvar translate=\"no\"\u003eFEATURE_TYPE\u003c/var\u003e: the type of text detection you need from the file. Allowed values are `TEXT_DETECTION` or `DOCUMENT_TEXT_DETECTION`.\n - \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: your project ID.\n5. Save the Python script.\n\n6. Run the Python script to detect text in the file:\n\n python \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eSCRIPT_NAME\u003c/span\u003e\u003c/var\u003e\n\n Replace \u003cvar translate=\"no\"\u003eSCRIPT_NAME\u003c/var\u003e with the name you gave to your\n Python script, such as `vision.py`."]]
