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you're migrating from another TRP implementation such as the [Textract Response Parser for Python](https://github.com/aws-samples/amazon-textract-response-parser/tree/master/src-python), please note that the APIs and available features may be substantially different. Please let us know if there's a feature you're missing!\n\n\n## Installation\n\nYou can use TRP in your JavaScript or TypeScript NPM projects:\n\n```sh\n$ npm install amazon-textract-response-parser\n```\n\n```js\n// With CommonJS-style require:\nconst { TextractDocument, TextractIdentity } = require(\"amazon-textract-response-parser\");\n// Or ES-style module imports:\nimport { TextractDocument, TextractExpense } from \"amazon-textract-response-parser\";\n```\n\n...Or link directly in the browser - for example via a CDN like [unpkg](https://unpkg.com/):\n\n```html\n<script src=\"https://unpkg.com/amazon-textract-response-parser@x.y.z\"></script>\n\n<script>\n  // Use the main parser classes:\n  var doc = new trp.TextractDocument(...);\n  // Or other exported utility functions/classes/enums/etc:\n  var avg = trp.aggregate([1, 2, 3], trp.AggregationMethod.Mean);\n</script>\n```\n\nTo enable this, the distribution of this library provides multiple builds:\n\n- `dist/cjs` (default `main`), for CommonJS environments like NodeJS - including most front end applications built with tools like React and Webpack.\n- `dist/es` (default `module`), for ES6/ES2015/esnext capable environments.\n- `dist/browser` (default `jsdelivr` and `unpkg`), for linking directly from browser HTML with no module framework (IIFE).\n\nThis means that **deep imports** will depend on your build environment, but are generally discouraged anyway and may not work correctly with TypeScript. Check out the [examples/](examples/README.md) folder on GitHub for some basic starters using the different styles.\n\n\n## Loading data\n\nInitialize a `TextractDocument` (or `TextractExpense`, `TextractIdentity`) by providing the parsed response JSON object from the corresponding [Amazon Textract APIs](https://docs.aws.amazon.com/textract/latest/dg/API_Reference.html) such as [GetDocumentAnalysis](https://docs.aws.amazon.com/textract/latest/dg/API_GetDocumentAnalysis.html), [AnalyzeID](https://docs.aws.amazon.com/textract/latest/dg/API_AnalyzeID.html), or [AnalyzeExpense](https://docs.aws.amazon.com/textract/latest/dg/API_AnalyzeExpense.html). In most cases, providing an **array** of response objects is also supported (for use when a large Amazon Textract response was split/paginated).\n\nFor example, loading a response JSON from file in NodeJS:\n\n```js\nfs.readFile(\"./my-analyze-document-response.json\", (err, resBuffer) => {\n  if (err) throw err;\n  const doc = new TextractDocument(JSON.parse(resBuffer));\n  // ...\n});\n```\n\nIf you're using TypeScript, you may need to **typecast** your input JSON while loading it.\n\n> The `ApiResponsePage` input interface exposed and expected by this module is more constrained than - but functionally compatible with - the output types produced by the [AWS SDK for JavaScript Textract Client](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/clients/client-textract/index.html).\n\n```typescript\nimport { ApiAnalyzeExpenseResponse } from \"amazon-textract-response-parser\";\nimport { TextractClient, AnalyzeExpenseCommand } from \"@aws-sdk/client-textract\";\nconst textract = new TextractClient({});\n\nasync function main() {\n  const textractResponse = await textract.send(\n    new AnalyzeExpenseCommand({\n      Document: { Bytes: await fs.readFile(\"...\") },\n    })\n  );\n  const expense = new TextractExpense((textractResponse as unknown) as ApiAnalyzeExpenseResponse);\n}\n```\n\nWith your data loaded in to a TRP `TextractDocument` or similar, you're ready to take advantage of the higher-level TRP.js functions to navigate and analyze the result.\n\n\n## Generic document text navigation\n\nIn general, this library avoids directly exposing **arrays** in results (see the *Mutation operations* section below). Instead, you can use:\n\n- `.n***` properties to count items\n- `.list***()` functions to return a copy of the underlying array\n- `.iter***()` functions to iterate through collections, or\n- `.***At***()` functions to fetch a specific item from a collection\n\nFor example:\n\n```typescript\n// Navigate the document hierarchy:\nconsole.log(`Opened doc with ${doc.nPages} pages`);\nconsole.log(\n  `The first word of the first line is ${doc.pageNumber(1).lineAtIndex(0).wordAtIndex(0).text}`\n);\n\n// Iterate through content:\nfor (const page of doc.iterPages()) {\n  // (In Textract's output order...)\n  for (const line of page.iterLines()) {\n    for (const word of line.iterWords()) {\n      console.log(word.text);\n    }\n  }\n}\n\n// ...Or get snapshot arrays instead of iterators, if you need:\nconst linesArrsByPage = doc.listPages().map((p) => p.listLines());\n```\n\nThese arrays are in the raw order returned by Amazon Textract, which is not necessarily a logical human reading order - especially for multi-column documents. See the *Layout analysis* and *List text in approximate reading order* sections below for extra content sorting utilities.\n\n\n## Queries\n\nThe results of [Amazon Textract Queries](https://docs.aws.amazon.com/textract/latest/dg/queryresponse.html) are accessible at the page level under `page.queries`. You can `get*` a query by exact question text or alias, or `search*` them by case-insensitive substrings:\n\n```typescript\ndoc.listPages().forEach((page) => {\n  // Log a quick human-readable overview of queries & answers:\n  console.log(page.queries.str());\n\n  // Get a query (and its top result's text) by exact alias:\n  const customer = page.queries.getQueryByAlias(\"customer_name\")?.topResult?.text;\n\n  // Get possible results of a query from most to least confident:\n  const shippingAddrCandidates =\n    page.queries.getQueryByAlias(\"shipping_addr\")?.listResultsByConfidence() || [];\n  const shippingAddrTopConf = shippingAddrCandidates[0].confidence;\n\n  // Seaching matches queries e.g. 'What is the Shipping Address?', 'FIND THE BILLING ADDRESS', etc\n  const addrQueries = page.queries.searchQueriesByQuestion(\"address\");\n});\n```\n\n\n## Forms (Key-Value pairs)\n\nAs well as looping through the [form data key-value pairs](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-kvp.html) in the document, you can query fields by key:\n\n```typescript\nconsole.log(doc.form.nFields);\nconst fields = doc.form.listFields();\n\n// Exact match:\nconst addr = doc.form.getFieldByKey(\"Address\").value?.text;\n\n// Search key containing (case-insensitive):\nconst addresses = doc.form.searchFieldsByKey(\"address\");\naddresses.forEach((addrField) => { console.log(addrField.key.text); });\n```\n\nNote that the `Field.confidence`, `FieldKey.confidence` and `FieldValue.confidence` scores reflect confidence of the **key-value structure detection** model. For aggregated OCR confidence of their **actual text**, use `.getOcrConfidence()` instead.\n\nYou can also search form keys at the individual page level, or look up the page number for detected fields:\n\n```typescript\nconst fieldByDoc = doc.form.getFieldByKey(\"Address\");\nconsole.log(`Detected Address on page ${fieldByDoc.parentPage.pageNumber}`);\n\nconst page = doc.pageNumber(1);\nconst fieldByPage = page.form.getFieldByKey(\"Address\");\n```\n\n`field.isCheckbox` is true for fields whose value contain exactly one SelectionElement object: meaning they're a (key=label)->(value=checkbox/radio) pair. For these fields, you can directly use `field.selectionStatus` or `field.isSelected` to look up the value's status. For other (non-checkbox) fields, they'll return `null`.\n\n\n## Tables\n\nThis library's table navigation tools address **[merged cells](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-tables.html) by default**, for convenience.\n\n```typescript\nconsole.log(page.nTables);\nconst table = page.tableAtIndex(0);\n\n// Index cells by row, column, or both:\nconst headerStrs = table.cellsAt(1, null)?.map(cell => cell.text);\nconst firstColCells = table.cellsAt(null, 1);\nconst targetCell = table.cellAt(2, 4);\n\n// Iterate over rows/cells:\nfor (const row of table.iterRows()) {\n  for (const cell of row.iterCells()) {\n    console.log(cell.text);\n  }\n}\n```\n\nFurther configuration arguments can be used to change the treatment of merged cells if needed:\n\n```typescript\n// Iterate over rows repeating any cells spanning multiple rows:\nfor (const row of table.iterRows({repeatMultiRowCells: true})) {}\n\n// Return split sub-cells instead of merged cells when indexing:\nconst firstColCellFragments = table.cellsAt(null, 1, {ignoreMerged: true});\n```\n\nThe `Table.confidence`, `Row.getConfidence()` and `Cell.confidence` scores reflect confidence of the **table structure detection** model. For aggregated OCR confidence of the text contained inside, use `.getOcrConfidence()` instead.\n\nUse `Table.tableType` and `Cell.hasEntityTypes()` to explore the more advanced [entity types](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-tables.html) extracted by Amazon Textract: For example column headers, title cells, footer cells, and summary cells:\n\n```typescript\nimport { ApiTableCellEntityType, ApiTableEntityType } from \"amazon-textract-response-parser\";\n\nconst isSemiStruct = table.tableType === ApiTableEntityType.SemiStructuredTable;\nconst colHeaders = table.rowAt(1).listCells()\n  .filter((c) => c.hasEntityTypes(ApiTableCellEntityType.ColumnHeader));\n```\n\nFor [overall table-level title and footer captions](https://aws.amazon.com/blogs/machine-learning/announcing-enhanced-table-extractions-with-amazon-textract/), see `table.listTitles()` and `table.listFooters()`, etc.\n\n\n## Layout analysis\n\n[Layout analysis in Amazon Textract](https://aws.amazon.com/blogs/machine-learning/amazon-textracts-new-layout-feature-introduces-efficiencies-in-general-purpose-and-generative-ai-document-processing-tasks/) detects higher-level semantic components than the core text Lines & Words - like paragraphs and headings. If you enabled this analysis, you can access the results through the `page.layout` collection:\n\n```typescript\n// Loop through content in implied reading order (from Layout API):\npage.layout.listItems().forEach((layItem) => {\n  console.log(layItem.blockType);  // There are different kinds of Layout Item\n  const textLines = layItem.listTextLines();  // All Layout* items can be queried for text LINEs\n  const children = layItem.listContent();  // Usually text LINEs, but sometimes other Layout* items\n  console.log(layItem.text + \"\\n\");  // ...Or you can just pull up the text\n});\n\n// Filtering by content type is also supported:\nfor (const layItem of page.layout.listItems({\n  skipBlockTypes: [\n    ApiBlockType.LayoutHeader, ApiBlockType.LayoutFooter, ApiBlockType.LayoutPageNumber\n  ],\n})) {\n  console.log(layItem.text);\n}\n```\n\nIf Forms and/or Tables analyses were also enabled, you'll be able to traverse from the relevant Layout object types to these more detailed representations. **However,** because these are separate analyses the correspondence may not be 1-to-1 and TRP is having to do some reconciliation under the hood:\n\n```typescript\nimport { ApiBlockType, LayoutKeyValue, LayoutTable } from \"amazon-textract-response-parser\";\n\npage.layout.listItems().forEach((layItem) => {\n  if (layItem.blockType === ApiBlockType.LayoutKeyValue) {\n    const fields = (layItem as LayoutKeyValue).listFields(); // Probably multiple\n    fields.forEach((field) => console.log(field.key.text));\n  } else if (layItem.blockType === ApiBlockType.LayoutTable) {\n    const tables = (layItem as LayoutTable).listTables(); // Probably just 1\n    tables.forEach((table) => console.log(table.nCells));\n  }\n});\n```\n\n\n### List text in approximate reading order (with or without `Layout`)\n\nParticularly for multi-column documents, the default output sequence for Amazon Textract `LINE`/`WORD` OCR results will likely not be the overall reading order you'd like. For best performance, enable and use the `Layout` analysis because **layout items are returned in implied reading order** as estimated by the AI service.\n\nAlternatively, TRP.js provides a **client-side heuristic algorithm** that can attempt to sort results without Layout. There are even some configuration parameters exposed to help you tune the results for your particular domain, and test harnesses in the [tests/unit/corpus folder](tests/unit/corpus) to help you experiment via `npm run test:unit`:\n\n```typescript\nimport { ReadingOrderLayoutMode } from \"amazon-textract-response-parser\";\n\n// By default, we automatically use `Layout` when it's available and heuristics when it's not:\nlet textInReadingOrder: string = page.getTextInReadingOrder();  // Just generate text\nlet pseudoParas = page.getLineClustersInReadingOrder();\n\n// You can force use of `Layout` (throwing an error if none available):\nlet layText = page.getTextInReadingOrder({ useLayout: ReadingOrderLayoutMode.RequireLayout });\n// Or fine-tune heuristic parameters:\nlet layParas = page.getLineClustersInReadingOrder({\n  colHOverlapThresh = 0.75,\n  paraVDistTol = 0.8,\n  // ...\n  useLayout: ReadingOrderLayoutMode.IgnoreLayout,\n});\n\n// Lines are clustered by \"paragraph\"/layout element:\nfor (const pseudoParagraph of pseudoParas) {\n  for (const line of pseudoParagraph) {\n    console.log(line.text);\n  }\n  console.log();  // Print a gap between \"paragraphs\"\n}\n```\n\nWhen configured to use Layout analysis results, these functions should be equivalent to just looping through your `page.layout.iterItems()` to get the text from each one in order.\n\n\n### Render documents to semantic markup/markdown\n\nIf you'd like to use AI/ML models to further post-process your Amazon Textract results, you have a choice between those that take text-only inputs - and \"multi-modal\" models that can also ingest structural information (see for example [this Amazon Comprehend feature](https://aws.amazon.com/about-aws/whats-new/2021/09/amazon-comprehend-extract-entities-native-format/) and [this Amazon SageMaker sample](https://github.com/aws-samples/amazon-textract-transformer-pipeline/tree/main)). While multi-modal models may work best on complex structured documents, the pace of research on text-only Large Language Models has historically been faster (perhaps because plain text data is easier to come by and work with).\n\n**Semantic markup like HTML** provides somewhat of a middle ground where we can try to preserve the layout/form/table/etc structure Amazon Textract extracted, but still provide plain text. This may be particularly useful for working with **Generative Large Language Models** (GenAI/LLMs) like those on [Amazon Bedrock](https://aws.amazon.com/bedrock/).\n\n```typescript\n// Render HTML for individual components:\nconsole.log(page.listTables[0].html());\n\n// ...Or for whole pages/documents:\nconst docHtml = doc.html();\nfs.writeFile(\"./my-doc.html\", docHtml, (err) => {});\n```\n\nSome caveats to be aware of:\n\n- Top-level `Page.html()` and `TextractDocument.html()` currently depend on Layout analysis being enabled, because the Layout results are used to sequence all the elements together.\n- Only HTML is supported currently, but we're keen to add `.markdown()` if there's interest\n\nYou can also **filter out** types of content you don't want to include in your HTML. \n\n```typescript\n// Most commonly, you'll `skip` high-level layout elements like `LayoutHeader`:\nconst docHtml = doc.html({\n  skipBlockTypes: [\n    ApiBlockType.LayoutHeader, ApiBlockType.LayoutFooter, ApiBlockType.LayoutPageNumber\n  ],\n});\n\n// Skipping lower-level blocks is also possible, but can produce weird results:\nconst docHtmlNoCellsOrSelectors = doc.html({\n  skipBlockTypes: [ApiBlockType.Cell, ApiBlockType.SelectionElement],\n});\n\n// Allow-listing is also possible, but you should include *everything* relevant:\nconst docTablesHtml = doc.html({\n  includeBlockTypes: [\n    ApiBlockType.Page,\n    ApiBlockType.LayoutTable,\n    ApiBlockType.Table,\n    ApiBlockType.Cell,\n    ApiBlockType.SelectionElement,\n    ApiBlockType.Word,\n  ],\n});\n```\n\nIf you have feedback about these features, please let us know in the GitHub issues to help prioritise!\n\n\n### Segment headers and footers from main content\n\nThis is another task for which you might find [Textract Layout analysis](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) useful - by looping through layout items and filtering out those of type `LayoutHeader`, `LayoutFooter` and `PageNumber`.\n\nHowever, TRP.js also provides a heuristic function you can try instead:\n\n```typescript\nconst segmented = page.getLinesByLayoutArea(\n  true  // (Also try to sort lines in reading order)\n);\n\nconsole.log(\"---- HEADER:\")\nconsole.log(segmented.header.map((l) => l.text).join(\"\\n\"));\nconsole.log(\"\\n---- CONTENT:\")\nconsole.log(segmented.content.map((l) => l.text).join(\"\\n\"));\nconsole.log(\"\\n---- FOOTER:\")\nconsole.log(segmented.footer.map((l) => l.text).join(\"\\n\"));\n```\n\n**Note:** Unlike the `*inReadingOrder` APIs, this utility has not yet been updated to use Textract Layout analysis when it's available. That behavior might change in future.\n\n\n### Calculate average skew of page text\n\nCalculating the overall skew of a page can be useful for validation checks: For example to detect and reject a strongly skewed image which might degrade the accuracy of tables, forms, or other downstream analyses.\n\n```typescript\n// Check the average angle/skew of detected text:\nconst skew = page.getModalWordOrientationDegrees();\n```\n\nThis method aggregates the skew to find the most common angle across all content on the page.\n\n\n## Signatures\n\nIf you enabled [signature detection in Amazon Textract](https://aws.amazon.com/blogs/machine-learning/detect-signatures-on-documents-or-images-using-the-signatures-feature-in-amazon-textract/), you can check for signatures at the page level:\n\n```typescript\n// e.g. print number of signatures detected by page:\ndoc.listPages()\n      .forEach((page, ix) => { console.log(`${page.nSignatures} signatures on page ${ix+1}`); });\n// ...Or get the position of the first signature on the first page:\nconst bbox = doc.pageNumber(1).listSignatures()[0].geometry.boundingBox;\n```\n\n\n## Expense (invoice and receipt) objects\n\nSince the format of responses for Amazon Textract's [Expense results](https://docs.aws.amazon.com/textract/latest/dg/expensedocuments.html) is very different from the [general document analysis APIs](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-document-layout.html), you can use the separate `TextractExpense` class in this library to process these.\n\n```typescript\nconst expense = new TextractExpense(textractResponse);\n\n// Iterate through content:\nconsole.log(`Found ${expense.nDocs} expense docs in file`);\nconst expenseDoc = [...expense.iterDocs()][0];\nfor (const group of expenseDoc.iterLineItemGroups()) {\n  for (const item of group.iterLineItems()) {\n    console.log(`Found line item with ${item.nFields} fields`);\n    for (const field of item.iterFields()) {\n      ...\n    }\n  }\n}\n\n// Get snapshot arrays instead of iterators, if you need:\nconst summaryFieldsArrByDoc = expense.listDocs().map((doc) => doc.listSummaryFields());\nconst linesArrsByPage = doc.listPages().map((p) => p.listLines())\n\n// Retrieve item fields by their tagged 'type':\nconst vendorNameFields = expenseDoc.searchSummaryFieldsByType(\"VENDOR_NAME\");\nconsole.log(`Found ${vendorNameFields.length} vendor name fields in doc summary`);\nconsole.log(vendorNameFields[0].fieldType.text); // \"VENDOR_NAME\"\nconsole.log(vendorNameFields[0].value.text); // e.g. \"Amazon.com\"\n```\n\n\n## Identity document objects\n\nSimilarly to expenses mentioned above, Amazon Textract offers specific APIs for [identity document analysis](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-identity.html). You can use the separate `TextractIdentity` class in this library to process these.\n\n```typescript\nimport { ApiAnalyzeIdResponse, TextractIdentity } from \"amazon-textract-response-parser\";\nimport { TextractClient, AnalyzeIDCommand } from \"@aws-sdk/client-textract\";\nconst textract = new TextractClient({});\n\nasync function main() {\n  const textractResponse = await textract.send(\n    new AnalyzeIDCommand({\n      Document: { Bytes: await fs.readFile(\"...\") },\n    })\n  );\n  const identity = new TextractIdentity((textractResponse as unknown) as ApiAnalyzeIdResponse);\n}\n```\n\nThe library implements some enumerations of known values (for field types, ID types, and so on) to make processing AnalyzeID responses a little simpler:\n\n```typescript\nimport { IdDocumentType, IdFieldType } from \"amazon-textract-response-parser\";\n\nconst idDoc = identity.getDocAtIndex(0); // (Or iterate, list docs in a result)\n\nif (idDoc.idType === IdDocumentType.Passport) {\n  // Fetch fields by known type:\n  const passNumField = idDoc.getFieldByType(IdFieldType.DocumentNumber);\n  console.log(\n    `Passport number ${passNumField.value}, confidence ${passNumField.valueConfidence}%`\n  );\n\n} else if (idDoc.idType === IdDocumentType.DrivingLicense) {\n  // ...Or list or iterate the document's fields:\n  for (const field of idDoc.iterFields()) {\n    console.log(`${field.fieldTypeRaw}: ${field.valueRaw}`);\n  }\n\n} else {\n  // Produce human-readable representations of fields, documents, or whole responses:\n  console.log(idDoc.str());\n}\n```\n\n\n## Mutation operations\n\nEasier analysis and querying of Textract results is useful, but what if you want to augment or edit your Textract JSONs with JS/TS Textract Response Parser?\n\nIn general:\n\n- Where the library classes (`TextractDocument`, `Page`, `Word`, etc) offer mutation operations, these should modify the source API JSON object **in-place** and ensure self-consistency.\n- For library classes that are backed by a specific object in the source API JSON, you can access it via the `.dict` property (`word.dict`, `table.dict`, etc) but then *you're* responsible for updating any required references in other objects if making changes there.\n- Any individual-block-level changes you make to the underlying API JSON should be dynamically reflected in the parsed TRP objects (e.g. overriding word text, coordinates, etc)... But changes that affect inter-block relationships are more likely to cause staleness issues.\n\nIn particular for **array properties**, you'll note that TRP generally exposes getters and iterators (such as `table.nRows`, `table.iterRows()`, `table.listRows()`, `table.cellsAt()`) rather than direct access to lists - to avoid implying that arbitrary array mutations (such as `table.rows.pop()`) are properly supported.\n\n\n## Other features and examples\n\nFor more examples on how to use the library, you can refer to the (basic) [examples](examples/) and (more complete) [test](test/) folders on GitHub, and the source code itself. If you have suggestions for additional features that would be useful, please open a GitHub issue!\n\n\n## Development\n\nThe integration tests for this library validate the end-to-end toolchain for calling Amazon Textract and parsing the result, so note that to run the full `npm run test` command:\n\n1. Your environment will need to be configured with a login to AWS (e.g. via the [AWS CLI](https://aws.amazon.com/cli/))\n2. Billable API requests may be made\n\nYou can alternatively run just the local/unit tests via `npm run test:unit`.\n","readmeFilename":"README.md"}