Evidence · first-party tested/Best AI Tools for Extracting Structured Data from PDFs and Business Documents
Derives transaction_type and transaction_id on transaction rows, classifying one record as UPI with transaction_id 917615251879 and cheque_number left empty.
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
Research media bank statement 2 jul.png
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, 4 other criteria
Extraction Accuracy✓ WorkedExtracts key statement values as typed fields, including account holder MR SEENIVASAN, account number 42710540422, and opening and closing balances 114453.65 and 116149.46.Extraction Accuracy✓ WorkedPreserves long disclaimer prose in dedicated fields, capturing two separate strings for insurance_coverage and reporting_period rather than collapsing them into one blob.Schema Adherence✓ WorkedReconstructs dense statement OCR into the requested nested JSON hierarchy, populating separate statement, account_holder, account, branch, balances, transactions, rewards, and disclaimers objects instead of flattening everything into text.Table & Record Completeness◐ MixedOvercounts the transaction table summary: total_transactions is 43, while the report says the expected count is 40 after excluding Balance Forward, tax, and charge entries.
Provenance
- Observation
- 0067b41d-0138-4fc0-80b1-151bb93e2539
- 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: "retab",
scenario: "financial-document-extraction"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Semantic Field Enrichment
Datalab✗ FailedDoes 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.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.Unstract✓ WorkedClassifies transaction_type correctly across the transaction array, using Deposit and Withdrawal labels rather than raw OCR text.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com