OCR
Active
GLM-OCR
GLM-OCR stands out by combining state-of-the-art computer vision with intelligent structure detection, delivering 95%+ accuracy on real-world documents.
Unlike traditional OCR engines that simply output raw text, GLM-OCR preserves formatting, hierarchy, and layout relationships.
GLM-OCR goes beyond character detection. It analyzes document layout and semantic structure, enabling accurate reconstruction of content in a format that mirrors the original document. Headings remain headings, lists stay structured, tables retain alignment, and multi-column layouts are interpreted correctly.
This structural awareness significantly reduces the need for post-processing. Instead of spending time cleaning unstructured text, developers and enterprises receive output that is immediately usable in automation pipelines, databases, content systems, or AI-powered applications.
GLM-OCR processes a wide variety of inputs, including scanned PDFs, photographed documents, screenshots, and multi-column layouts. It performs reliably even on documents that mix text blocks, tables, and graphical elements.
Tables are often the most challenging component of document digitization. GLM-OCR detects table boundaries, column relationships, header structures, and multi-row formatting with high precision. Instead of flattening tables into fragmented lines of text, it reconstructs them in structured Markdown format, preserving alignment and hierarchy.
GLM-OCR seamlessly handles multi-page documents while maintaining section continuity and structural hierarchy. Page order is preserved, and logical relationships between sections remain intact.
GLM-OCR supports standard image formats such as JPEG and PNG, along with scanned and digital PDF files. Multi-page processing is handled natively, eliminating the need for page-by-page orchestration.
The output is delivered as structured Markdown, designed to preserve formatting and document hierarchy while remaining easy to transform into other structured formats when required.
Automate repetitive document processing tasks in finance, legal, HR, and operations. Extract structured data directly from PDFs and scanned files without manual entry.
Feed clean Markdown into search indexes, retrieval-augmented generation systems, and enterprise AI assistants to improve accuracy and reduce noise.
Extract line items, totals, dates, and structured financial data from invoices and statements with formatting preserved.
Convert scanned academic papers and multi-page research documents into structured, searchable text.
GLM-OCR goes beyond character detection. It analyzes document layout and semantic structure, enabling accurate reconstruction of content in a format that mirrors the original document. Headings remain headings, lists stay structured, tables retain alignment, and multi-column layouts are interpreted correctly.
This structural awareness significantly reduces the need for post-processing. Instead of spending time cleaning unstructured text, developers and enterprises receive output that is immediately usable in automation pipelines, databases, content systems, or AI-powered applications.
GLM-OCR processes a wide variety of inputs, including scanned PDFs, photographed documents, screenshots, and multi-column layouts. It performs reliably even on documents that mix text blocks, tables, and graphical elements.
Tables are often the most challenging component of document digitization. GLM-OCR detects table boundaries, column relationships, header structures, and multi-row formatting with high precision. Instead of flattening tables into fragmented lines of text, it reconstructs them in structured Markdown format, preserving alignment and hierarchy.
GLM-OCR seamlessly handles multi-page documents while maintaining section continuity and structural hierarchy. Page order is preserved, and logical relationships between sections remain intact.
GLM-OCR supports standard image formats such as JPEG and PNG, along with scanned and digital PDF files. Multi-page processing is handled natively, eliminating the need for page-by-page orchestration.
The output is delivered as structured Markdown, designed to preserve formatting and document hierarchy while remaining easy to transform into other structured formats when required.
Automate repetitive document processing tasks in finance, legal, HR, and operations. Extract structured data directly from PDFs and scanned files without manual entry.
Feed clean Markdown into search indexes, retrieval-augmented generation systems, and enterprise AI assistants to improve accuracy and reduce noise.
Extract line items, totals, dates, and structured financial data from invoices and statements with formatting preserved.
Convert scanned academic papers and multi-page research documents into structured, searchable text.
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