Text line recognition

If you do not want to send an entire image to Vision OCR, you can cut out a single line and send it for recognition.

Getting startedGetting started

To use the examples, install cURL.

Get your account data for authentication:

Recognizing a text line using the OCR APIRecognizing a text line using the OCR API

The OCR API allows you to recognize a separate line using the recognize method.

To recognize a single line of text in an image, follow the Text recognition in images guide.

Recognizing a text line using the Vision APIRecognizing a text line using the Vision API

The Vision API allows you to recognize a separate line using the recognize method.

In the configuration, specify the line model to recognize text lines more accurately.

To recognize a line of text:

1. Encode the file as Base64:

   UNIX

   Windows

   PowerShell

   Python

   Node.js

   Java

   Go

   base64 -i input.jpg > output.txt
   

   C:> Base64.exe -e input.jpg > output.txt
   

   [Convert]::ToBase64String([IO.File]::ReadAllBytes("./input.jpg")) > output.txt
   

   # Import a library for encoding files in Base64
   import base64
   
   # Create a function that will encode a file and return results.
   def encode_file(file_path):
     with open(file_path, "rb") as fid:
         file_content = fid.read()
     return base64.b64encode(file_content).decode("utf-8")
   

   // Read the file contents to memory.
   var fs = require('fs');
   var file = fs.readFileSync('/path/to/file');
   
   // Get the file contents in Base64 format.
   var encoded = Buffer.from(file).toString('base64');
   

   // Import a library for encoding files in Base64.
   import org.apache.commons.codec.binary.Base64;
   
   // Get the file contents in Base64 format.
   byte[] fileData = Base64.encodeBase64(yourFile.getBytes());
   

   import (
       "bufio"
       "encoding/base64"
       "io/ioutil"
       "os"
   )
   
   // Open the file.
   f, _ := os.Open("/path/to/file")
   
   // Read the file contents.
   reader := bufio.NewReader(f)
   content, _ := ioutil.ReadAll(reader)
   
   // Get the file contents in Base64 format.
   base64.StdEncoding.EncodeToString(content)
   

2. Create a file with the request body, e.g., body.json:

   body.json:

   {
       "folderId": "b1gvmob95yys********",
       "analyze_specs": [{
           "content": "iVBORw0KGgo...",
           "features": [{
               "type": "TEXT_DETECTION",
               "text_detection_config": {
                   "language_codes": ["*"],
                   "model": "line"
               }
           }]
       }]
   }
   

   Where:

   • folderId: ID of any folder for which your account has the ai.vision.user role or higher.
   • content: Image file contents encoded as Base64.
   • model: Line model.

3. Send a request using the batchAnalyze method and save the response to a file, e.g., output.json:

   Bash

   CMD

   PowerShell

   export IAM_TOKEN=<IAM_token>
   curl \
     --request POST \
     --header "Content-Type: application/json" \
     --header "Authorization: Bearer ${IAM_TOKEN}" \
     --data '@body.json' \
     https://vision.api.cloud.yandex.net/vision/v1/batchAnalyze > output.json
   

   set IAM_TOKEN=<IAM_token>
   curl ^
     --request POST ^
     --header "Content-Type: application/json" ^
     --header "Authorization: Bearer %IAM_TOKEN%" ^
     --data "@body.json" ^
     https://vision.api.cloud.yandex.net/vision/v1/batchAnalyze > output.json
   

   $Env:IAM_TOKEN="<IAM_token>"
   curl `
     --request POST `
     --header "Content-Type: application/json" `
     --header "Authorization: Bearer $Env:IAM_TOKEN" `
     --data '@body.json' `
     https://vision.api.cloud.yandex.net/vision/v1/batchAnalyze > output.json