Evidence · first-party tested/Best AI Tools for Extracting Structured Data from PDFs and Business Documents

Does not populate schema-derived transaction identifiers at all: the 18 Jun withdrawal keeps transaction_id = null even though the identifier is visible in the source row.

✗ Failed🧾 artifact-verifiedinput + output shownTest date not recordedDatalab →

What was measured

Semantic Field Enrichment

Are derived fields — transaction_type, transaction_id, cheque_number, day patterns, ad codes — correctly classified or extracted beyond raw OCR?

decisive for this rankingtransformation

This ranking is not just about copying OCR text; it also depends on whether the tool can correctly infer or classify document-specific fields needed for useful structured output. (3 of 3 judges)

What was given, what came back

Test input: Bank Statement PDF · pdf · group: financial-document-extraction

Input — what we sent

Input file 3 — as supplied

Bank Statement PDF.pdf

Bank Statement PDF

A 4-page bank statement PDF with 51 transactions, balances, rewards, and disclaimer text, used to test schema-driven extraction of dense financial tables and multi-page continuity.

Why this input is hard

· Table extraction across 50+ transaction rows
· Multi-page continuity with BALANCE FORWARD bridges
· Structured metadata vs. free-text transaction descriptions
· Numerical accuracy for balances, deposits, withdrawals, and summaries
· Nested schema population for account, branch, balances, rewards, and disclaimers

Output — unretouched

Also checked on this input — same tool, 5 other criteria

Extraction Accuracy✓ WorkedExtracts the visible document-level values correctly, including State Bank of India, 16 Jul 2019, INR, account number 42710540422, account type SMART BANKING SAVINGS ACCOUNT, branch Rajaji Salai, MICR 600036005, IFSC SCBL0036078, and phone 25349005.Schema Adherence✓ WorkedMaps the bank statement into the requested nested JSON hierarchy instead of flattening it into OCR text, and preserves field-level citation metadata on the extracted objects.Table & Record Completeness✗ FailedOver-extracts the transaction table, returning 54 transactions where the statement was expected to yield 51, which indicates extra continuation or split records were emitted.Table & Record Completeness✗ FailedMerges adjacent statement rows into one overly long transaction description, breaking row boundaries on the 21 Jun example instead of keeping the two transactions separate.Table & Record Completeness✗ FailedCan attach a transaction to the wrong date when rows are merged, with an 18 Jun 19 withdrawal appearing under a 19 Jun 19 record in the extracted output.

Provenance

Observation: 6028e5fa-9c96-4f9c-ad9d-5cc900225ace
Evidence run: ec4d736d-95f9-4c88-884c-e280435f7b7b
Study: Extract and query structured data from documents using natural language
Research task: 86b9y25e5
Tested at: not recorded
Source: first-party
Evidence state: verified
Proof shown: input + output shown
Cost / latency: not captured
Repeat run: not captured
Tester: not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this

get_evidence({
  tool: "datalab",
  scenario: "financial-document-extraction"
})

MCP · mcp.aidemos.com/api/mcp

JSON · ai.aidemos.com/v1/observations/6028e5fa-9c96-4f9c-ad9d-5cc900225ace

Free with attribution.

Same input, same check — 7 other tools

measured on Semantic Field Enrichment

Extend AI✗ FailedIt leaves derived `transaction_id` values as `null` even when reference identifiers are present in the description, so identifier extraction does not generalize.Landing AI✓ WorkedAdds meaningful transaction_type labels to extracted rows, classifying the sample 18 Jun records as Withdrawal, Withdrawal, and Deposit instead of leaving the field as raw OCR text.LlamaParse✗ FailedFails to derive transaction-level fields, leaving transaction_id and transaction_type empty even for descriptions that encode ATM, UPI, and CRADJ cues.Nanonets✗ FailedIt does not populate the derived transaction_type field, leaving it null across the statement instead of classifying deposits and withdrawals.Reducto✗ FailedLeaves derived transaction metadata incomplete, with transaction_type and transaction_id missing across transaction rows.Retab✓ WorkedDerives transaction_type and transaction_id on transaction rows, classifying one record as UPI with transaction_id 917615251879 and cheque_number left empty.Unstract✓ WorkedClassifies transaction_type correctly across the transaction array, using Deposit and Withdrawal labels rather than raw OCR text.

This evidence is published in

Best AI Tools for Extracting Structured Data from PDFs and Business Documents →unstract →

Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com