TXT to JSON Converter

What Is TXT to JSON Conversion?

TXT to JSON conversion turns unstructured or semi-structured text into a JSON object or array. Unlike a fixed file-format conversion, plain text does not define one universal structure, so the converter has to apply explicit parsing rules.

A line list, a key-value file, and a CSV-like table are all text, but they should produce different JSON shapes. Choosing the correct parsing mode matters more than changing the file extension.

Choose the Right Parsing Mode

• Lines to array: use when each non-empty line is one string value.
• Key-value text: use when each line holds a key and a value separated by a colon or an equals sign.
• Delimited rows: use when the text has columns separated by commas, tabs, semicolons, or pipes.

Convert Lines to a JSON Array

Input:

Alice
Bob
Charlie

Output:

["Alice", "Bob", "Charlie"]

This mode suits lists of IDs, URLs, emails, tags, and keywords. The array keeps the input order, and Remove duplicate lines drops later duplicates while keeping the first occurrence in place.

Convert Key-Value Text to a JSON Object

Input:

name: Alice
age: 30
active: true

Output with Infer basic types enabled:

{
  "name": "Alice",
  "age": 30,
  "active": true
}

With the option off, which is the default, the output is:

{
  "name": "Alice",
  "age": "30",
  "active": "true"
}

Key-value parsing splits each line at the first separator only. A line such as url: https://example.com/a:b yields the key url and the value https://example.com/a:b, rather than breaking the URL apart. Lines with no separator, or with an empty key, are skipped and counted in the preview instead of failing the conversion.

Convert Delimited Text to JSON Records

Input:

name,age,city
Alice,30,New York
Bob,25,London

Output:

[
  {"name":"Alice","age":"30","city":"New York"},
  {"name":"Bob","age":"25","city":"London"}
]

Use a CSV-compatible parser when fields can contain delimiters, escaped quotes, or line breaks. In delimited mode, quoted fields are supported: a value wrapped in double quotes may contain the delimiter and line breaks, and two consecutive double quotes inside it represent one literal quote. A simple split on the delimiter is not sufficient for that kind of data.

Type Inference Can Change Values

Plain text carries no reliable type system. This converter only infers a narrow set of values, and only when Infer basic types is enabled: lowercase true and false become booleans, and plain integers or decimals matching -?\d+ or -?\d+\.\d+ become numbers.

• Leading zeros: 00123 stays the string "00123".
• Version strings: 1.10 would lose its trailing zero as a number, so it stays text when inference is off.
• Uppercase and null-like text: TRUE, FALSE, and NULL are not recognized and stay strings.
• Scientific notation: 1e5 is not recognized and stays a string.
• Dates and postal codes: these are treated as text unless you enable inference and they happen to match the numeric patterns.

Keep type inference disabled when preserving the exact source text matters more than convenience.

Headers and Uneven Rows

Delimited input may contain a header row, missing values, extra values, or blank headers. When First row is header is off, or a header cell is blank, the converter generates the names column_1, column_2, and so on. Rows with a different number of columns are highlighted in the preview and reported as a warning; the conversion still runs, missing cells become empty values, and extra values beyond the column count are ignored.

Text to JSON Is Not Natural-Language Understanding

Plain text can be ambiguous. A paragraph, a log file, a Markdown document, or an AI response cannot become a meaningful JSON structure without rules. Use an explicit pattern such as one item per line, key-value pairs, or a consistent delimiter. For AI-generated output, first make sure it follows a predictable structure.

TXT to JSON vs JSON String Parsing

TXT to JSON can mean several things. This page handles structured plain text and text files. If the input is already a JSON-encoded string such as "{\"name\":\"Alice\"}", use a JSON unescape workflow instead. If the input is an ordinary JSON document, use a JSON formatter or validator rather than a text converter.

TXT to JSON vs JSONL

A JSON array stores many values inside one JSON document. JSON Lines stores one JSON value per line. This page outputs a JSON array or a JSON object and does not provide a JSONL output mode, so if a downstream system needs JSONL, convert it with a separate step.

Privacy and Browser-Based Processing

This converter reads text and generates JSON in the browser rather than uploading the input to a remote service. That is useful for internal lists and development data, but it is not a secret-management system. Avoid entering credentials, tokens, private customer records, or confidential production text into any online tool.

When to Use a Local Parser

For very large files, repeatable data pipelines, strict CSV compatibility, confidential data, or automated processing, use a local parser or command-line workflow. Browser conversion is best suited to quick transformations, inspection, and small-to-medium inputs.