Top 10 Best Text Extraction Software of 2026

Veryfi focuses on extracting purchase documents, including invoices and receipts, then mapping recognized text into structured fields such as vendor, invoice identifiers, dates, totals, and line items. The workflow supports both machine-first extraction and a human-in-the-loop review path when confidence drops. Multi-page documents work as a single capture unit so field totals and item tables can be assembled across pages.

A notable tradeoff is that field mapping accuracy depends heavily on document layout consistency and capture quality, so atypical templates often require review. Veryfi is a strong fit when automation needs to land in accounting or reconciliation systems with predictable field schemas and repeatable document ingestion.

Teams use Nanonets to extract text and structured data from multi-page files, then map results into fields used for search, auditing, or workflow triggers. Human-in-the-loop review helps correct low-confidence results, which is practical when document layouts vary across suppliers or business units. Configuration is typically done through guided setup that couples labeled examples with extraction rules and output templates.

A key tradeoff is that accurate extraction depends on providing enough representative documents and iterating when formats change. Nanonets fits best when documents arrive repeatedly with consistent document types, like invoices and forms, and when an operations team can manage model updates through the review workflow.

Rossum is built for high-throughput document processing where fields, tables, and totals need repeatable extraction across varying layouts. Extraction quality is managed through iterative training workflows that use labeled corrections to improve recognition for document classes and templates. Human-in-the-loop review is a first-class step, which helps when confidence scores are low or documents deviate from the training set.

A tradeoff is that higher accuracy typically requires setup time to define document types, map extracted fields, and feed corrections back into the model. Rossum fits best for invoice capture and other structured forms where organizations already know the document categories and want faster throughput without replacing their existing OCR or storage layers.

OCRmyPDF converts scanned PDFs into searchable PDFs by running OCR on each page and writing recognized text back into the output. The tool includes preprocessing steps like deskew and image cleanup to improve OCR results on angled or noisy scans.

It is automation-friendly because it is driven from a command-line interface and supports batch processing across folders of PDF files. The project also fits into developer workflows through scriptable execution rather than a hosted interface.

Amazon Textract extracts text from documents using OCR plus layout analysis to identify key text regions and structure. It also returns structured outputs such as forms data through key-value extraction and table detection, which reduces the need for custom parsing.

Image inputs can be processed in batch through the AWS API and paired with downstream automation in workflows. Confidence scores and human review loops support document QA when extraction quality varies by scan quality or layout complexity.

Google Cloud Document AI turns document images and PDFs into structured output using Google-managed models for layout-aware extraction. It supports form-like data capture with key-value extraction and table extraction, and it can generate confidence scores alongside fields for downstream decisioning.

The REST API and event-driven integration options fit batch pipelines and human-in-the-loop review workflows where output must be stored with traceable results. It is a strong fit when extraction quality must be managed through model selection, input normalization, and repeatable processing configurations.

Azure AI Document Intelligence combines an OCR engine with layout-aware processing that produces structured outputs for forms, invoices, and multi-page documents. It offers a REST API for model invocation and batch-style workflows, plus model-driven extraction like key-value and table capture.

Configuration options support document language handling and confidence scoring for downstream human review. Integration with Azure services supports event-driven automation and governed access through Azure identity controls.

UiPath Document Understanding focuses on intelligent document processing built for end-to-end automation in the UiPath ecosystem. Its key capabilities include layout analysis for extracting structured fields and workflows that route documents for human-in-the-loop review based on confidence scoring.

The tool supports multi-page document processing and can produce searchable PDF output after text recognition. Integration depth comes from UiPath orchestration and extension points that connect extraction results into downstream automations and systems.

Foxit PDF Editor handles PDF text extraction and scanned-document OCR inside an editor workflow focused on turning documents into selectable text. It supports multi-page OCR with image cleanup steps like deskew to improve text recognition quality on uneven scans.

Foxit also provides searchable PDF output and form-oriented extraction workflows suited to invoices and other structured documents. The product’s value shows up when document cleanup, recognition, and edit-and-export steps must stay in one operator flow.

Tungsten TotalAgility is a document capture and workflow automation tool aimed at enterprise document processing teams with high-volume back offices. It combines configurable document ingestion with processing steps for classification, text extraction, and structured field capture, then routes results into downstream systems via integrations and APIs.

Its differentiator is the depth of end-to-end automation around document workflows rather than extraction alone. Human-in-the-loop review and operational controls support audit-friendly handling of low-confidence pages across multi-page submissions.