AI powered OCR for documents

Engaging Creative Minds Via Technology

Automate and streamline document-centric processes, freeing up valuable time and resources for businesses to focus on higher-value tasks.

AI-Powered & Cloud Optimized

Powered by a suite of proprietary, state-of-the-art AI algorithms with highest standard for data processing and security.

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Use Case for Every Industry

Improve efficiency across various industries by automating the extraction of data from documents.

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AI with human touch

Combines AI automation with human oversight, ensuring accuracy, handling exceptions with experienced team.

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Advanced Digitization

Integrate cutting-edge AI-powered OCR to unlock superior text recognition, versatile PDF conversion, and comprehensive data extraction within your application.

Document OCR: Enhance document clarity, deskew the image and extract text including handwritten, checkboxes, currency symbols.
Document Parsing: Hierarchical text components, including words, lines, and paragraphs with page coordinates, from multi-column layouts.
Unstructuted to Structured: Ingest document in varied formats such as .pdf, .png, .jpeg, .jpg, .tiff, .heic, .xlsx, .txt, .html.

Table Detection and Extraction

Locate tables and analyze complex table structures by decomposing them into rows, columns, cells, spanning cells, and headers.

Accurate Table Detection: Automatically identifies table regions within documents, regardless of format including multi column pages.
Complex Table Structure Recognition: Handles tables with merged cells, varying column widths, missing borders, and complex layouts.
Output Formats: Generates structured output in formats like HTML, JSON, or markdown, making it easy to integrate with downstream applications.

Power of Document Graph for Large Language Models (LLMs)

Document Graph enhances LLMs by improving contextual understanding, information retrieval, data integration, and efficient document processing.

Document Graph

Document graph represent document’s content in a graph structure where nodes represent various elements such as text, tables, and images, and edges represent the relationships between these elements for nuanced understanding of the document’s content and its interconnections.