Image to Excel#

Extract tables from images and generate .xlsx files.

Use Cases#

Use when users upload images containing tables and request "convert to table", "extract table", or "generate Excel".

Workflow#

1. Analyze Image#

Use the image tool to load the user-uploaded image, with a prompt requesting row-by-row, column-by-column table data extraction:

text

Extract all table data from the image row by row and column by column, returning it as a JSON array.
Format: [[row1col1, row1col2, ...], [row2col1, row2col2, ...], ...]
Notes:
- The first row may be a header; keep it as-is
- Split merged cells into individual cells and fill with the same value
- Preserve original formatting for amounts and numbers
- Use empty string "" for missing cells

2. Data Validation & Correction#

After receiving the JSON data from the image model, check and correct:

• Row/column count consistency (fill in missing cells)
• Number format correctness (remove extra spaces, unify decimal points)
• Chinese character recognition accuracy (fix obvious typos)
• Header completeness (infer missing headers from content)

Store the corrected data in the variable rows.

3. Write to Excel#

Call the generation script with the corrected data:

bash

python3 scripts/gen_excel.py <output_path> '<json_data>'

• output_path: Output path, e.g., /root/.openclaw/workspace/table_extracted.xlsx
• json_data: Corrected 2D array as a JSON string

4. Output Results#

Inform the user of the file save location and display a preview of the first 5 rows for confirmation.