Pretrained overview
Document AI offers multiple products to process documents for information for different use cases.
Pretrained parsers
For more information, go to Explore pretrained processors.
Bank statement parser
Bank statement parser extracts key-value pairs (KVP). It can extract up to 17 generic entities. Examples include: Account number, client name, bank name, and table items like deposits and withdrawals. You don't specify the fields (schema) you want to extract. Bank statement parser supports Enrichment and Normalization.
W2 parser
W2 parser extracts from the IRS Form W2 as KVP. It can extract up to 12 generic entities, including employee name, Social Security Number, employer, and wages. You don't specify the fields (schema) you want to extract. W2 parser supports Enrichment.
US passport parser
US passport parser extracts KVP. It can extract up to seven generic entities. These include given names, family names, document ID, and date of birth. You don't specify the fields (schema) you want to extract. US passport parser supports Normalization.
Utility parser
Utility parser extracts KVP. It can extract up to 75 generic entities from utility bills. These include supplier name, previous paid amount, and line items like amount, description, and product code and quantity. You don't specify the fields (schema) you want to extract with the utility parser.
Identity document proofing parser
Identity document proofing parser predicts the validity of ID documents using multiple signals.
fraud_signals_is_identity_document
detection: Predicts whether an image contains a recognized identity document.fraud_signals_suspicious_words
detection: Predicts whether words are present that aren't typical on IDs.fraud_signals_image_manipulation
detection: Predicts whether the image was altered or tampered with an image editing tool.fraud_signals_online_duplicate
detection: Predicts whether the image can be found online (US only).
Pay slip parser
Pay slip parser extracts KVP. It can extract up to 26 generic entities from pay slips. These include employee name, bonus, commissions, overtime, and pay date. You don't specify the fields (schema) you want to extract. Pay slip parser supports Enrichment and Normalization.
US driver license parser
US driver license parser extracts KVP. It can extract up to eight generic entities from a driver license. Examples include: Given name, family name, document ID, and expiration date. You don't specify the fields (schema) you want to extract. US driver license parser supports Normalization.
Expense parser
Expense parser extracts KVP. It can extract up to 17 generic entities from expense reports. Examples include: Expense date, supplier name, total amount, and currency. You don't specify the fields (schema) you want to extract. Expense parser supports Enrichment and Normalization.
Invoice Parser
Invoice Parser extracts KVP. It can extract up to 46 generic entities from invoices. These include invoice number, supplier name, invoice amount, tax amount, invoice date, and due date. You don't specify the fields (schema) you want to extract. Invoice Parser supports Enrichment and Normalization.
Summarizer
Summarizer gives abstract and bullet point summaries for short and long documents. Summarizer also lets you specify the output length of the summary as comprehensive, medium, or brief.