How Can AI Extract Fields from Word Tables to Excel in Bulk?

In day-to-day work we often deal with Word tables—student registration forms, customer info sheets, sign-up forms, and more. These tables come in many formats, but they usually share one need: extract the key field values into a standard Excel file.

Manual copy-paste is slow and error-prone. When you face dozens or hundreds of Word files, the workload grows exponentially. So—can we use AI to automatically extract fields from differently structured Word tables and consolidate them into Excel?

The answer: absolutely.

1) Background: Data Lives in Word, But We Need “Structured Fields”

Many Word tables contain structured data, yet they’re hard to move straight into Excel because:

• Layouts vary (two-column key–value, multi-row headers, merged cells, nested tables).
• Field labels are inconsistent (e.g., “Phone”, “Mobile”, “Contact No.”).
• Some values are in paragraphs or lists near the table, not inside cells.
• There may be multiple tables per file with different structures.

Our tool Chestnuttable was built to fix exactly this.

2) The Chestnuttable Solution: Field Extraction + Structure Recognition + Batch Export

Goal:

• Identify field–value pairs across varied table layouts.
• Normalize field names (e.g., map “Tel”, “Phone”, “Mobile” to Phone).
• Export a clean, standardized Excel for downstream analysis.

3) How It Works

from docx import Document

doc = Document("example.docx")
records = []
for table in doc.tables:
    for row in table.rows:
        cells = [cell.text.strip() for cell in row.cells]
        # store candidate key-value pairs
        if len(cells) == 2 and cells[0] and cells[1]:
            records.append({"field": cells[0], "value": cells[1]})

import pandas as pd

data_list = [("a.docx", r["field"], r["value"]) for r in records]

df = pd.DataFrame(data_list, columns=["File", "Field", "Value"])
df.to_excel("result.xlsx", index=False)

4) Batch Processing for Multiple Word Files

Upload a folder or select many files; the system processes every table and merges results automatically.

Enhanced capabilities include:

• Handling merged cells, nested tables, and multi-row headers.
• Field normalization and deduplication across files.
• Value validation (e.g., phone/email formats) and simple cleaning.
• Multilingual field mapping (Chinese–English, etc.).
• Configurable field dictionaries and regex-based extraction.

5) Real-World Use Cases

6) Try Chestnuttable

Visit our site and try it in one click:

Just:

1. Upload one or more Word files (or a folder).
2. Confirm field mapping (optional) and preview results.
3. Export the standardized Excel.

7) Summary

Chestnuttable is an AI-powered table structure recognizer that delivers:

• Automatic field–value pairing across varied layouts.
• Robust structure recognition and normalization.
• Fast batch export to Excel.
• High accuracy with minimal manual tweaks.

It solves the long-standing “manual retyping” problem for office workflows.

Coming next:

• PDF table recognition and image-table OCR.
• Richer validation rules and custom templates.
• API access for system integration.

If you process a lot of Word tables, give Chestnuttable a try. Have complex scenarios? Reach out— we’ll keep polishing the product so AI truly serves frontline work.