Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Halaman ini menunjukkan cara mendeteksi teks gambar menggunakan
Optical Character Recognition (OCR) API di perangkat air-gapped Google Distributed Cloud (GDC).
Layanan OCR Vertex AI di perangkat air-gapped GDC mendeteksi teks dalam gambar menggunakan metode API BatchAnnotateImages. Layanan ini mendukung file JPEG dan PNG untuk gambar.
Sebelum memulai
Sebelum dapat mulai menggunakan OCR API, Anda harus memiliki project yang mengaktifkan OCR API dan memiliki kredensial yang sesuai.
Anda juga dapat menginstal library klien untuk membantu Anda melakukan panggilan ke API. Untuk
informasi selengkapnya, lihat Menyiapkan project pengenalan karakter.
Mendeteksi teks dari file JPEG dan PNG
Metode BatchAnnotateImages mendeteksi teks dari batch file JPEG atau PNG.
Anda mengirimkan file yang ingin Anda deteksi teksnya secara langsung sebagai konten dalam permintaan API. Sistem menampilkan teks yang terdeteksi dalam format JSON dalam respons API.
Anda harus menentukan nilai untuk kolom dalam isi JSON permintaan API Anda. Tabel berikut berisi deskripsi kolom isi permintaan yang harus Anda berikan saat menggunakan metode API BatchAnnotateImages untuk permintaan deteksi teks:
Kolom isi permintaan
Deskripsi kolom
content
Gambar dengan teks yang akan dideteksi. Anda memberikan representasi Base64 (string ASCII) dari data gambar biner Anda.
type
Jenis deteksi teks yang Anda butuhkan dari gambar.
Tentukan salah satu dari dua fitur anotasi:
TEXT_DETECTION mendeteksi dan mengekstrak teks dari gambar apa pun. Respons JSON mencakup string yang diekstrak, setiap kata, dan kotak pembatasnya.
DOCUMENT_TEXT_DETECTION juga mengekstrak teks dari gambar, tetapi layanan ini mengoptimalkan respons untuk teks dan dokumen yang padat. JSON mencakup informasi halaman, blok, paragraf, kata, dan jeda.
Opsional. Daftar bahasa yang akan digunakan untuk deteksi teks.
Sistem menafsirkan nilai kosong untuk kolom ini sebagai deteksi bahasa otomatis.
Anda tidak perlu menetapkan kolom language_hints untuk bahasa yang didasarkan pada alfabet Latin.
Jika Anda mengetahui bahasa teks dalam gambar, menyetel petunjuk akan meningkatkan hasil.
Membuat permintaan API
Buat permintaan ke API terlatih OCR menggunakan metode REST API. Atau, berinteraksi dengan API yang telah dilatih sebelumnya untuk OCR dari skrip Python guna mendeteksi teks dari file JPEG atau PNG.
Contoh berikut menunjukkan cara mendeteksi teks dalam gambar menggunakan
OCR:
REST
Ikuti langkah-langkah berikut untuk mendeteksi teks dalam gambar menggunakan metode REST API:
Simpan file request.json berikut untuk isi permintaan Anda:
BASE64_ENCODED_IMAGE: representasi Base64
(string ASCII) dari data gambar biner Anda. String ini dimulai dengan
karakter yang terlihat mirip dengan
/9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==.
FEATURE_TYPE: jenis deteksi teks yang Anda perlukan dari gambar. Nilai yang diizinkan adalah TEXT_DETECTION atau
DOCUMENT_TEXT_DETECTION.
LANGUAGE_HINT: tag bahasa BCP 47 yang akan digunakan sebagai
petunjuk bahasa untuk deteksi teks, seperti en-t-i0-handwrit. Kolom ini bersifat opsional dan sistem menafsirkan nilai kosong sebagai deteksi bahasa otomatis.
Tambahkan kode berikut ke skrip Python yang Anda buat:
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)image={"content":"BASE64_ENCODED_IMAGE"}features=[{"type_":vision.Feature.Type.FEATURE_TYPE}]# Each requests element corresponds to a single image. To annotate more# images, create a request element for each image and add it to# the array of requestsreq={"image":image,"features":features}metadata=[("x-goog-user-project","projects/PROJECT_ID")]resp=vc.annotate_image(req,metadata=metadata)print(resp)if__name__=="__main__":creds=main()vision_func(creds)
Ganti kode berikut:
ENDPOINT: endpoint OCR yang Anda gunakan untuk organisasi Anda. Untuk mengetahui informasi selengkapnya, lihat status dan endpoint layanan.
BASE64_ENCODED_IMAGE: representasi Base64
(string ASCII) dari data gambar biner Anda. String ini dimulai dengan
karakter yang terlihat mirip dengan
/9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==.
FEATURE_TYPE: jenis deteksi teks yang Anda perlukan dari gambar. Nilai yang diizinkan adalah TEXT_DETECTION atau
DOCUMENT_TEXT_DETECTION.
PROJECT_ID: project ID Anda.
Simpan skrip Python.
Jalankan skrip Python untuk mendeteksi teks dalam gambar:
pythonSCRIPT_NAME
Ganti SCRIPT_NAME dengan nama yang Anda berikan ke skrip
Python, seperti vision.py.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[],[],null,["# Detect text in images\n\nThis page shows you how to detect image text 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 images using the\n`BatchAnnotateImages` API method. The service supports JPEG and PNG files for\nimages.\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 from JPEG and PNG files\n-----------------------------------\n\nThe `BatchAnnotateImages` method detects text from a batch of JPEG or PNG 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 `BatchAnnotateImages` API method for your text\ndetection requests:\n\n### Make an 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 JPEG or PNG files.\n| **Note:** The `BatchAnnotateImages` API method only supports a single request per batch call.\n\nThe following examples show how to detect text in an image using\nOCR: \n\n### REST\n\nFollow these steps to detect text in images 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 \"image\": {\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_IMAGE\u003c/span\u003e\u003c/var\u003e\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 }\n ]\n }\n EOF\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eBASE64_ENCODED_IMAGE\u003c/var\u003e: the Base64 representation (ASCII string) of your binary image data. 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 image. 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/images: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/images: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 an image:\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 image = {\"content\": \"\u003cvar translate=\"no\"\u003eBASE64_ENCODED_IMAGE\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 image. To annotate more\n # images, create a request element for each image and add it to\n # the array of requests\n req = {\"image\": image, \"features\": features}\n\n metadata = [(\"x-goog-user-project\", \"projects/\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e\")]\n\n resp = vc.annotate_image(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_IMAGE\u003c/var\u003e: the Base64 representation (ASCII string) of your binary image data. 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 image. 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 image:\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`."]]
