Apryse Server SDK release notes

v12.0

The Server SDK introduces new OCR and XLIFF Reflow updates, enhancing what customers can do with documents entirely inside their own environment. The new OCR engine raises text-extraction accuracy on real-world documents and widens language coverage, turning scanned and image-based files into searchable, structured data. XLIFF Reflow adds right-to-left (RTL) language support, so translation workflows can render Arabic and Hebrew layouts automatically rather than through manual rework. Across both, the work runs server-side on the customer’s own infrastructure and feeds directly into the broader document pipeline they already build on.

We’re excited to announce the new AI-powered OCR engine for the Apryse Server SDK. It significantly improves accuracy on real-world documents and performs better on low-quality scans and complex layouts. Language coverage expands to include CJK, Portuguese, and additional Latin-alphabet languages. Extracted text is available as structured JSON or XML for downstream search, indexing, and AI pipelines – all processed on-premise or server-side, cross-platform, so documents never leave the customer's environment.

Use cases include:

• Invoices, receipts, and other financial documents.
• Forms and applications.
• Scanned records in healthcare and legal workflows.
• Older archived PDFs that need to be made searchable.
• Feeding extracted, structured data into search and indexing, LLM or RAG pipelines, and internal databases.

The new OCR is the default OCR module, while the pre-existing V11 OCR is now an alternative module. When you download the OCR module you’ll download either the default or alternative OCR module, depending on which you’d like to use.

See the following documentation for developing this feature:

• Review our OCR overview.
• Try out OCR code samples available in multiple languages.
• Refer to OCR APIs available in C++, Java, Node.js and .NET.

XLIFF Reflow adds RTL language support, letting customers apply RTL translations, Arabic and Hebrew, to left-to-right (LTR) source documents. Page layouts mirror automatically, reading order is preserved across complex designs, and mixed-direction text: numbers, dates, and embedded Latin, follows correct bidirectional rules.

RTL languages don’t only translate differently; they flow differently. Handled manually, this puts teams into cycles of rework, layout fixes, and extended QA that delay launches and raise costs. With our new XLIFF Reflow RTL language support, users see formatting fidelity from extraction to reinsertion; end-to-end XLIFF automation with the same workflow as before, now RTL-aware.

Use cases include:

• Translating an LTR source document into an RTL language.
• Localizing regulatory materials, customer invoices, or policy statements into RTL languages.
• Scaling multilingual PDF production into RTL markets without branching document templates or manual layout correction.

See the following documentation for developing this feature:

• Enable XLIFF document reflow
• Try out document translation reflow samples

See our latest Changelog for more Server release information, including new APIs and bug fixes.

See our downloads page to access the most recent versions of our products so you have all of the latest features and functionality available.

v11.12

In this release, we continue to build on the Apryse SDK server-side capabilities of:

• Smart Data Extraction with Handwriting Intelligent Character Recognition.
• XLIFF Reflow for translating PDFs now supports the updated XLIFF 2.0 specification.
• Native file conversion for MSG and EML files into standard PDFs.
• RTF-to-PDF file conversion to bring legacy RTF documents into PDF format.
• PDF sanitization to remove hidden, sensitive content from PDF files.

We’re excited to announce Intelligent Character Recognition (ICR) to the Apryse Server SDK, enabling automated extraction of handwritten text from documents traditionally resistant to digitization. This enhancement closes a critical gap in end‑to‑end automation workflows by converting previously inaccessible handwritten content into structured, searchable data.

The benefits of using this feature include:

• Adds support for machine‑learning‑based handwriting interpretation.
• Handles highly unstructured inputs, including medical forms, insurance claims, historical and archival documents, logistics, and shipping records.
• Produces JSON output suitable for downstream automation and analytics.
• Unlocks handwritten content previously excluded from digital workflows with true end-to-end automation.
• Runs ICR entirely within your secure infrastructure via the Apryse Server SDK locally—no external APIs, no data exposure, and no unpredictable usage costs.
• Builds scalable architecture on Apryse’s high‑performance, cloud‑agnostic SDK.

See the following documentation for developing this feature:

• Review our ICR overview
• Review Handwriting Intelligent Character Recognition (ICR) Workflows Guide
• Try out ICR code samples available in multiple languages
• Refer to ICR APIs available in C++, Java, Node.js, and .NET

We’re excited to announce we now fully support compatibility with the XLIFF 2.0 specification which enables the use of a modern, structured XML‑based localization format. XLIFF 2.0 enhances metadata clarity and contextual fidelity within translation units, improving interoperability across localization systems and workflows. This results in more maintainable translation assets, increased consistency across platforms, and improved efficiency for large‑scale, multilingual document processing.

The benefits of using this feature include:

• Reliable preservation of reading order across complex layout structures.
• Consistent formatting fidelity from extraction to reinsertion.
• Stable handling of varied typographic and document elements.
• End-to-end automation that reduces manual QA and layout correction.

See the following documentation for developing this feature:

• Enable XLIFF document reflow
• Try out document translation reflow samples

We’re excited to announce Email File Conversion, enabling developers to convert .eml and .msg files into standardized, high‑fidelity PDFs. This feature preserves full message context—including metadata, message structure, and attachments—without relying on Microsoft Outlook automation.

Additionally, the document conversion suite now includes Rich Text Format (RTF) to PDF support, allowing legacy RTF documents to be migrated into modern PDF workflows with minimal setup.

The benefits of using the Email File Conversion feature include:

• Preserves full message metadata (sender, recipients, timestamps, subject).
• Retains email thread structure for clarity and traceability.
• Produces consistent, searchable PDF output suitable for compliance and archival workflows.
• Eliminates dependency on Outlook‑based conversion processes.

Email attachments are processed and preserved as part of the resulting PDF:

• Insert PDF attachments directly into the final document for seamless viewing.
• Embed non‑PDF attachments (e.g., DOCX, images) in their original formats.
• Bundle all attachments within a single PDF container to maintain communication integrity.
• Ensure email records—including all related files—can be archived, processed, or reviewed without risk of data separation.

The conversion suite now supports RTF to PDF, enabling older, or, legacy documents, to be integrated into modern workflows:

• Maintain original formatting with high-fidelity PDF output.
• Create searchable PDFs for improved indexing, retrieval, and accessibility.
• Integrate legacy repositories or archival systems with minimal configuration.

See the following documentation for developing this feature:

• Refer to supported files for desktop and mobile devices
• Try out .EML and .MSG File code samples available in multiple languages
• Refer to Microsoft Office Conversion APIs available in multiple languages

We’re excited to announce a dedicated PDF Sanitization API which provides programmatic control over sensitive data removal to improve document security and compliance.

The benefits of using this feature include:

• Adds an automated security layer to document workflows by programmatically and selectively scrubbing a PDF’s internal structure.
• Removes metadata, markup annotations, embedded scripts, obscured texts, or sensitive data.

See the following documentation for developing this feature:

• Review the PDF sanitization overview
• How to sanitize a PDF document
• Try out our code samples available in multiple languages
• Refer to the Sanitization APIs available in C++, Java, Node.js, and .NET

See our latest Changelog for more Server release information, including new APIs and bug fixes.

See our downloads page to access the most recent versions of our products so you have all of the latest features and functionality available.

v11.8.0
In this release, we continue to build on the server-side Smart Data Extraction (SDE) features which turn documents and their metadata into powerful tools for downstream system consumption.

We’re excited to announce Document Classification, an AI-trained SDK API that identifies each file on upload based on a predefined set of 18 categories, so you can validate intake, route to the right workflow, or add metadata for processing later. The output includes the predicted label with a confidence score in structured JSON for easy integration into your solution.

The benefits of using this feature include:

• Automatically identify document types from a predefined set of 18 categories such as invoices, receipts, IDs, budgets, contracts, and more.
• Detects and assigns each page a type and confidence score.
• You decide the thresholds for automated routing or manual reviews.
• Provides easy integration into downstream workflows.

See the following documentation for developing this feature:

• Review core capabilities and key use cases
• Set up Document Classification guide
• Reduce package size for the Smart Data Extraction module
• Automatically identify and extract meaningful key-value pairs from PDFs
• Install and configure the Data Extraction Module
• Find and extract Smart Data from PDF samples

We’re excited to announce CAD Title Block Extraction, an AI-powered API that automatically detects and extracts key information from engineering drawings. Building upon our Key-Value Extraction, it's designed to help architecture, engineering, and construction (AEC) projects by eliminating the need for time-consuming manual searches and data entry.

The tool uses a custom trained AI model to locate the title block in a drawing and extract the important metadata such as company details, project and creator information, drawing number, revision, date, and professional stamps or licenses. Once extracted, this metadata is output into structured JSON format, which can be used by software providers and AEC organizations to create efficient search and indexing systems.

The benefits of using this feature include:

• Transform static PDF drawings into structured data to easily search, track, audit, and synchronize across systems.
• Automate to reduce manual effort and human data entry error.
• Provide enhanced scalability without manual intervention or client-side software.

See the following documentation for developing this feature:

• Set up Smart Data Extraction guide
• Find and extract Smart Data from PDF samples

See our latest Changelog for more Server SDK release information, including new APIs and bug fixes.

v11.6.0
In this release, we continue to build on the server-side PDF editing capability with a scalable and automated search-and-replace functionality for PDFs with layout-preserving reflow. This allows organizations to apply consistent changes (e.g. brand updates, regulatory revisions, contractual terms) without compromising design or introducing manual editing risk.

We’re excited to announce programmatic text editing that enables precise search-and-replace in PDFs while preserving the original layout, fonts, and structure. Programmatic text editing uses Apryse’s native PDF editing engine to find and replace text content. Page layout is recalculated dynamically, so changes are absorbed into the document without affecting visual quality or structure. You can automate complex document edits at scale using this new feature.

The benefits of using this feature include:

• Automating changes to PDFs.
• Batch processing across high volumes of PDFs.
• Control over sensitive content since all the processing happens on the server.
• No need for client-side dependencies or third-party tools.
• Retaining original font, formatting, and visual fidelity across edits.

See the following documentation for developing this feature:

• Find and replace text in PDFs guide
• Find and replace text in PDFs samples

See our latest Changelog for more Server SDK release information, including bug fixes.