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HomeBackend DevelopmentPHP TutorialHow to use PHP and Alibaba Cloud OCR to implement image text recognition?

How to use PHP and Alibaba Cloud OCR to implement image text recognition?

Jul 17, 2023 pm 03:28 PM
phpAlibaba cloud ocrImage text recognition

How to use PHP and Alibaba Cloud OCR to implement image text recognition?

1. Introduction
With the development of artificial intelligence, image and text recognition has become a popular technology. With the help of Alibaba Cloud OCR (Optical Character Recognition) service, we can easily convert text in images into editable text format. This article will introduce how to use PHP and Alibaba Cloud OCR to implement image text recognition, and explain each step in detail in code examples.

2. Preparation work
Before we start, we need to complete the following preparation work:

  1. Register an Alibaba Cloud account: Register on the Alibaba Cloud official website and create an account.
  2. Activate the Alibaba Cloud OCR service: Log in to the Alibaba Cloud console, search for "OCR", find "Text Recognition OCR" in the results and click to enter. Follow the prompts to complete the service activation process.
  3. Obtain the API key: In the Alibaba Cloud console, select "AccessKey Management" and click the "Create AccessKey" button. Save the obtained "AccessKey ID" and "AccessKey Secret".

3. Install necessary dependencies
Before starting to use PHP and Alibaba Cloud OCR, we need to install some necessary dependency libraries. Execute the following command in the terminal to install the relevant dependencies:

$ composer require alibabacloud/sdk

4. Implement image text recognition
The following is a sample code that uses PHP and Alibaba Cloud OCR to implement image text recognition:

<?php
require_once 'vendor/autoload.php';
use AlibabaCloudClientAlibabaCloud;
use AlibabaCloudClientExceptionClientException;
use AlibabaCloudClientExceptionServerException;

// 设置阿里云账号的AccessKey信息
AlibabaCloud::accessKeyClient('your-access-key-id', 'your-access-key-secret')
    ->regionId('cn-hangzhou')
    ->asDefaultClient();

try {
    $result = AlibabaCloud::rpc()
        ->product('Ocr')
        ->scheme('https') // 开启HTTPS协议
        ->version('2015-11-11')
        ->action('RecognizeCharacter')
        ->method('POST')
        ->host('ocr.aliyuncs.com')
        ->options([
            'query' => [
                'RegionId' => 'cn-hangzhou',
                'ImageUrl' => 'https://example.com/image.jpg' // 待识别的图像URL
            ]
        ])
        ->request();
    
    // 解析识别结果
    $response = json_decode($result->getBody(), true);
    
    // 输出识别结果
    foreach ($response['Data']['OCRText'] as $text) {
        echo $text . "
";
    }
} catch (ClientException $e) {
    echo $e->getErrorMessage() . "
";
} catch (ServerException $e) {
    echo $e->getErrorMessage() . "
";
}
?>

In the above sample code, we first introduced the autoload file of Alibaba Cloud SDK using the require_once statement and imported the related namespace. Then, use the accessKeyClient method to set the AccessKey information of the Alibaba Cloud account and the region used. Next, call the rpc method to create an RPC request, and then use the version method to specify the API version to use. Then, use the action method to set the API action to be called. Use the options method to pass query parameters, where ImageUrl is the image URL to be recognized. Call the request method to initiate a request and obtain the response result. Finally, the recognition results are output by parsing the response results and traversing the recognition text.

5. Summary
This article briefly introduces how to use PHP and Alibaba Cloud OCR to implement image text recognition. We have completed the preparation work by registering an Alibaba Cloud account, activating the Alibaba Cloud OCR service, and obtaining the API key. Then, by installing the necessary dependencies and code samples, the image text recognition function is implemented. I hope this article can be helpful to you. For more details, please refer to the official Alibaba Cloud documentation.

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