After extracting a table from an image, the most important format decision is not “which file looks nicest?” It is “what needs to happen next?” A spreadsheet analyst, a command-line pipeline, and an API ingestion job have different requirements even when they start from the same photographed table.
LoveOCR provides Image to TSV, Image to CSV and Image to Excel tools because there is no universal winner. TSV and CSV are plain-text interchange formats. XLSX is a richer spreadsheet container that can preserve worksheets, formulas, styles, cell types and other workbook features. Choosing well can reduce cleanup and accidental type conversion later.
Choose TSV when delimiter collisions are your main concern
TSV separates fields with tab characters. It is particularly convenient when cells contain many commas: personal names written “surname, given name,” postal addresses, prose descriptions, citations, or values using commas for punctuation. Those commas are ordinary content in TSV rather than potential field separators.
TSV is also comfortable in many developer and data-science workflows because it is plain text, diffable, streamable and easy to parse. The tradeoff is that tabs and newlines inside values need an agreed escaping policy. Some desktop applications also associate CSV more readily than TSV, so compatibility with the receiver still matters.
Choose CSV for broad plain-text compatibility
CSV is widely supported by spreadsheets, databases, analytics products and business systems. If a vendor or import screen explicitly asks for CSV, use CSV rather than converting to TSV simply because tabs feel cleaner. Interoperability is more valuable than personal delimiter preference.
The catch is that CSV is a family of conventions rather than one behavior used identically everywhere. Comma delimiters, semicolon delimiters, quoting rules, embedded newlines, encodings and regional decimal conventions can differ. A good CSV workflow therefore records the delimiter and encoding and tests the result with the actual destination.
Choose XLSX when people will work in a spreadsheet
XLSX is usually the more practical destination when someone needs to sort, filter, add formulas, create charts, format columns, keep multiple sheets, or review questionable OCR values visually. Cell types can be controlled more explicitly, and a workbook can contain supporting notes that plain TSV or CSV cannot represent naturally.
That richness comes with complexity. XLSX is not human-readable plain text, is less convenient for version-control diffs, and can encourage automatic formatting that hides source issues. For example, a spreadsheet may display a long identifier in scientific notation or interpret 03/04 as a date. You still need type-aware import and review.
Compare the formats by the job they perform
| Need | TSV | CSV | XLSX |
|---|---|---|---|
| Plain-text pipeline | Strong | Strong | Usually unnecessary |
| Comma-heavy text | Convenient | Needs quoting discipline | No delimiter issue |
| Human spreadsheet editing | Basic import | Basic import | Strong |
| Version-control diff | Easy | Easy | Difficult |
| Multiple sheets/styles/formulas | No | No | Yes |
| Strict vendor import | Only if requested | Very common | Common in office workflows |
Think about data types before exporting
Neither TSV nor CSV carries a strong schema for “this field is an identifier, not a number.” The consumer decides how to interpret text. XLSX can store cell types, but OCR-generated workbooks still require review. In all three formats, classify important columns before use: identifiers, dates, decimals, currency, percentages, free text and boolean/status fields.
Leading zeros are a classic example. A source value such as 000381 may be a valid product code. If a spreadsheet or import routine coerces it to 381, the characters were recognized correctly but the workflow still damaged the data. The best format is the one whose next consumer you can configure reliably.
Consider scale and automation
For thousands of files flowing through scripts, plain text is attractive because it is lightweight and easy to inspect with standard tools. TSV can reduce quoting around comma-heavy prose; CSV can match existing data contracts. For occasional office work where a person needs to inspect and correct cells, XLSX can save time because the data arrives in a familiar grid.
If you need both automation and human QA, a useful pattern is to preserve a plain-text canonical export for pipelines and create a spreadsheet review copy. Do not allow two corrected versions to diverge without deciding which one is the source of truth.
Make the destination decide
Use TSV and test escaping/newline behavior with that loader.
Use CSV and follow its exact delimiter, quoting and encoding rules.
Use XLSX and review cell types before calculations.
TSV or CSV is easier to diff and review than a binary workbook.
Consider JSON, XML or another structured format instead of forcing nested data into rows and columns.
Keep the original image whichever format you choose
Format conversion is not evidence that OCR was correct. Keep the source image long enough to resolve questionable cells and preserve provenance. Then validate row counts, headers, high-impact values and business rules. The output format should make the next task easier; it should never make the source unverifiable.
Privacy and responsible handling
LoveOCR states that uploaded and generated files are transferred securely and automatically removed from its servers within three hours. That reduces temporary server retention, but it does not replace your own data-handling responsibilities. Only process material you are authorized to use, avoid exposing secrets or personal information unnecessarily, and store downloaded results according to the rules that apply to your project or organization.
For code, database definitions, structured data, and machine-readable exports, treat generated output as a starting point that still needs human review. A file can be syntactically valid while being semantically wrong. Compare important names, identifiers, numbers, relationships, URLs, and business facts with the source before you execute, publish, import, or automate anything.
Related LoveOCR resources
Frequently asked questions
Is TSV more accurate than CSV for OCR?
The OCR recognition step can be the same; TSV mainly changes how extracted fields are delimited and transported.
When is TSV better than CSV?
It is convenient when cells contain many commas or when a downstream tool explicitly expects tab-delimited text.
When should I use XLSX instead?
Use XLSX when people need spreadsheet features such as formulas, filters, styles, charts or multiple worksheets.
Can CSV or TSV preserve formulas?
Not as spreadsheet formulas in the way XLSX does; they are plain-text tabular formats.
Should I convert one format into another later?
Yes when needed, but validate types and delimiters during the conversion so identifiers, dates and multiline text are not silently changed.
Editorial note: This guide is based on the documented behavior of LoveOCR’s Image to TSV tool and focuses on validation, limitations, and practical downstream use instead of promising perfect output.
Updated: August 29, 2026 · Published by LoveOCR.
Choose the table format that fits
Start with the format your next application expects, then validate structure and data types against the source image.
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