URL Decode Online

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What is URL Decoding?

URL decoding, also known as percent decoding, is the process of converting URL-encoded strings back into their original form. URL encoding is used to represent special characters and non-ASCII characters in URLs and query parameters, and URL decoding is performed to retrieve the original data that was encoded.

The process of URL decoding involves identifying the '%' sign followed by two hexadecimal digits in the URL-encoded string and replacing them with the corresponding ASCII character. This process is repeated for each occurrence of '%XX' in the string, where 'XX' represents the hexadecimal code.

Here are a few examples of URL decoding: 

                    %20 -> Space (' ')
                    %3F -> Question Mark ('?')
                    %26 -> Ampersand ('&')

URL decoding is necessary to correctly interpret data that contains encoded special characters. When a URL-encoded string is received by a web server or application, it needs to be decoded before processing to ensure accurate data interpretation.

For example, consider a URL-encoded search query: 

                    Encoded URL: https://example.com/search?q=my%20search%20query%26category%3Delectronics
                    Decoded URL: https://example.com/search?q=my search query&category=electronics

The encoded URL is URL decoded to retrieve the original search query and category values, including the spaces and the ampersand that were originally present.

URL decoding is an essential part of web development, particularly when handling query parameters, form submissions, and data received from URLs. Many programming languages and web frameworks provide functions or methods for performing URL decoding operations automatically.

Keep in mind that URL decoding should be performed on trusted data, as improperly decoded data may lead to unexpected behavior or security vulnerabilities. Additionally, URL decoding should not be confused with HTML decoding, which is used to represent special characters within HTML documents.

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