Docyield's timesheet parser turns a completed timesheet — a scan, a photo, or a PDF — into structured rows ready for payroll. A timesheet records how an employee spent a pay period: who they are, who they work for, the dates the period covers, and a line for each block of time worked, broken down by date, project, start and end times, hours, and any notes. The parser reads all of that and returns it as CSV, Excel, or JSON, so the hours go straight into a payroll run instead of being keyed by hand.
Timesheets are notoriously varied. One team uses a printed weekly grid, another a handwritten card clocked in and out by pen, another a screenshot exported from a scheduling app. Columns sit in different orders, times appear as 9-5 on one sheet and 09:00-17:00 on another, and projects may be codes or full client names. A fixed-template reader stumbles on each variation. Docyield reads the sheet by what each column means, pulling out the per-day entries and the period total regardless of the layout.
What a timesheet captures
A timesheet is the record that connects worked time to pay. The header identifies the employee and the employer and fixes the period with a start and end date. The body is a list of entries — one per worked block — and this is where the detail that drives payroll lives: the date worked, the project, task, or client the time was charged to, the start and end times, the hours, and any note explaining the entry.
The period total then sums the hours. That total is the figure a payroll clerk reconciles against the line entries, and it is the number that ultimately feeds the pay calculation. Because the entries and the total both matter, the parser captures the full breakdown rather than just the bottom-line hours.
Why structured extraction beats manual entry and raw OCR
Re-keying a timesheet into payroll is slow and unforgiving. A row read in the wrong order, an end time mistaken for a start time, or hours dropped from one day quietly changes someone's pay. Across a team, the manual approach also does not scale — every fortnight the same grids come back to be retyped.
Plain OCR is not enough on its own, because a timesheet is a table and OCR returns its cells as loose text with no sense of which value is a date, which is an hours figure, and which row they belong to. Structured extraction rebuilds the rows: each entry comes back with its date, project, times, and hours in the right fields, so the data lands in payroll as clean records rather than a wall of numbers to re-sort.
Who uses a timesheet parser
- Payroll teams turning submitted timesheets into hours for a pay run without retyping.
- Agencies and consultancies billing client projects from the hours logged against each one.
- Construction, field-service, and shift businesses processing paper or handwritten time cards.
- Bookkeepers reconciling contractor hours against invoices and budgets.
- Operations managers tracking project time across a team to spreadsheets.
- Developers adding timesheet capture to a payroll or workforce app via the API.
Per-day entries and the period total
The heart of the output is the list of entries, and it is worth describing how those nested rows come back. Each entry is a self-contained record: the date it covers, the project or client the time was charged to, the start and end times where the sheet records them, the hours worked, and any free-text note. Exported to CSV or Excel, each entry becomes its own row, so you can sort by date, group by project, and sum hours with ordinary spreadsheet formulas.
Above the entries sit the header fields — employee, employer, and the period start and end — and below them the total hours for the period. Keeping the per-day entries and the period total as distinct values lets you check one against the other: if the entries do not add up to the stated total, that is exactly the kind of discrepancy a payroll reviewer wants to see. The project field on each entry is what makes billing possible: by charging hours to a client or task, the same sheet that drives payroll can also be grouped by project to produce a billing breakdown, all from one parse.
Accuracy, handwriting, and review
No parser reads every sheet perfectly, and timesheets include some of the harder inputs — handwritten cards in particular. Accuracy is highest on clean printed or digital sheets; handwritten times and hurried entries are where a figure can be misread, and a clearer scan or a steadier photo improves the result. Times written in shorthand are interpreted in context, but ambiguous ones deserve a glance.
Where a cell is blank — a missing note, an entry with no project — the field comes back empty rather than filled with a guess, since an invented project code or a fabricated hour is worse than a gap a reviewer can fill. Because the parser keeps the source sheet beside the extracted entries, checking the total against the rows takes only a moment on the sheets that warrant it. Sheets that record start and end times but leave the hours column blank are read in context where the arithmetic is clear, but an entry that spans a break or an overnight shift is exactly the sort of edge case a reviewer should confirm.
Output formats, API, and batch
Each parse exports as CSV, Excel, JSON, or XML from the same result. CSV and Excel are the natural fit here, since the entries become spreadsheet rows that drop straight into a payroll worksheet; JSON suits a payroll or workforce integration; XML fits an older time-and-attendance import. The free tool handles one timesheet at a time.
When a pay run brings in a whole team's sheets at once, the Docyield API and batch dashboard run the same extraction at scale, return results by webhook, and let you apply your own checks — for example flagging any sheet whose entry hours do not match the stated total. The field names are identical between the free tool and the API.
What the timesheet parser extracts
Each timesheet is returned against a fixed schema. Cells the sheet leaves blank come back empty rather than guessed. The entries are a nested list, with one record per worked block.
- Employee name
- The name of the employee the timesheet belongs to.
- Employer name
- The employer or company name.
- Period start
- The start date of the timesheet period.
- Period end
- The end date of the timesheet period.
- Entries
- A nested list of worked blocks. Each entry holds its date, the project, task, or client, the start and end times, the hours worked, and any note.
- Total hours
- The total hours recorded for the period.
How to convert a timesheet to CSV, Excel, or JSON
- 1Upload your timesheet — drop a scan, photo, or PDF of the sheet onto the box above, or choose a file.
- 2Wait a few seconds while Docyield reads the grid and rebuilds each entry as a row.
- 3Review the structured result, checking the per-day hours against the stated period total.
- 4Pick your output tab — CSV or Excel for payroll, or JSON and XML for integrations.
- 5Copy the result or download the file, ready to import into your payroll or workforce system.
Frequently asked questions
Processing documents at scale?
Batch upload, an extraction API, and webhooks for 100+ documents a month.