Handwriting recognition

You can recognize handwritten text in an image using the OCR API with the handwritten recognition model. With this model, you can recognize any combination of handwritten and typed text in Russian and English.

Getting startedGetting started

To use the examples, install cURL.

Get your account data for authentication:

Recognizing handwritten text in an imageRecognizing handwritten text in an image

Image text recognition is implemented in the recognize OCR API method.

1. Prepare an image file that meets the requirements:

   • The supported file formats are JPEG, PNG, and PDF. Specify the MIME type of the file in the mime_type property. The default value is image.
   • The maximum file size is 10 MB.
   • The image size should not exceed 20 MP (height × width).

2. Encode the image 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)
   

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

   body.json:

   {
     "mimeType": "JPEG",
     "languageCodes": ["ru","en"],
     "model": "handwritten",
     "content": "<base64-encoded_image>"
   }
   

   In the content property, specify the image file contents encoded as Base64.

4. UNIX

   Python

   export IAM_TOKEN=<IAM_token>
   curl \
     --request POST \
     --header "Content-Type: application/json" \
     --header "Authorization: Bearer ${IAM_TOKEN}" \
     --header "x-folder-id: <folder_ID>" \
     --header "x-data-logging-enabled: true" \
     --data "@body.json" \
     https://ocr.api.cloud.yandex.net/ocr/v1/recognizeText \
     --output output.json
   

   Where:

   • <IAM_token>: Previously obtained IAM token.
   • <folder_ID>: Previously obtained folder ID.

   data = {"mimeType": <mime_type>,
           "languageCodes": ["*"],
           "content": content}
   
   url = "https://ocr.api.cloud.yandex.net/ocr/v1/recognizeText"
   
   headers= {"Content-Type": "application/json",
             "Authorization": "Bearer {:s}".format(<IAM_token>),
             "x-folder-id": "<folder_ID>",
             "x-data-logging-enabled": "true"}
     w = requests.post(url=url, headers=headers, data=json.dumps(data))
   

   The result will consist of recognized blocks of text, lines, and words with their position on the image:

   {
     "result": {
       "textAnnotation": {
         "width": "241",
         "height": "162",
         "blocks": [
           {
             "boundingBox": {
               "vertices": [
                 {
                   "x": "28",
                   "y": "8"
                 },
                 {
                   "x": "28",
                   "y": "130"
                 },
                 {
                   "x": "240",
                   "y": "130"
                 },
                 {
                   "x": "240",
                   "y": "8"
                 }
               ]
             },
             "lines": [
               {
                 "boundingBox": {
                   "vertices": [
                     {
                       "x": "28",
                       "y": "8"
                     },
                     {
                       "x": "28",
                       "y": "77"
                     },
                     {
                       "x": "240",
                       "y": "77"
                     },
                     {
                       "x": "240",
                       "y": "8"
                     }
                   ]
                 },
                 "text": "Hello,",
                 "words": [
                   {
                     "boundingBox": {
                       "vertices": [
                         {
                           "x": "28",
                           "y": "9"
                         },
                         {
                           "x": "28",
                           "y": "81"
                         },
                         {
                           "x": "240",
                           "y": "81"
                         },
                         {
                           "x": "240",
                           "y": "9"
                         }
                       ]
                     },
                     "text": "Hello,",
                     "entityIndex": "-1",
                     "textSegments": [
                       {
                         "startIndex": "0",
                         "length": "7"
                       }
                     ]
                   }
                 ],
                 "textSegments": [
                   {
                     "startIndex": "0",
                     "length": "7"
                   }
                 ]
               },
               {
                 "boundingBox": {
                   "vertices": [
                     {
                       "x": "112",
                       "y": "94"
                     },
                     {
                       "x": "112",
                       "y": "130"
                     },
                     {
                       "x": "240",
                       "y": "130"
                     },
                     {
                       "x": "240",
                       "y": "94"
                     }
                   ]
                 },
                 "text": "World!",
                 "words": [
                   {
                     "boundingBox": {
                       "vertices": [
                         {
                           "x": "112",
                           "y": "89"
                         },
                         {
                           "x": "112",
                           "y": "137"
                         },
                         {
                           "x": "240",
                           "y": "137"
                         },
                         {
                           "x": "240",
                           "y": "89"
                         }
                       ]
                     },
                     "text": "World!",
                     "entityIndex": "-1",
                     "textSegments": [
                       {
                         "startIndex": "8",
                         "length": "4"
                       }
                     ]
                   }
                 ],
                 "textSegments": [
                   {
                     "startIndex": "8",
                     "length": "4"
                   }
                 ]
               }
             ],
             "languages": [
               {
                 "languageCode": "ru"
               }
             ],
             "textSegments": [
               {
                 "startIndex": "0",
                 "length": "12"
               }
             ]
           }
         ],
         "entities": [],
         "tables": [],
         "fullText": "Hello,\nWorld!\n"
       },
       "page": "0"
     }
   }
   

5. To get all the recognized words in an image, find all the values with the text property.