Indra OCR is a lightweight 1M-parameter model that converts PDFs and images to markdown, text, or JSON at 49 pages per second on CPU—roughly 12-24x faster than Tesseract and PaddleOCR. It handles layout detection, bounding boxes, and can read digital PDFs' text layers while OCRing only missing content.

• Speed and size: Processes 49 pages/second on CPU with ~230ms latency per page, weighs just 18MB, and uses ONNX Runtime instead of PyTorch
• Accuracy beats competitors: Scores 41.3 on OmniParseBench text (vs. PaddleOCR's 35.8 and Tesseract's 19.3) and 62.1 on headline score (vs. PaddleOCR's 58.4)
• Layout intelligence: Detects and labels blocks (headings, tables, lists, captions, footnotes) with bounding boxes and reading order, plus optional LaTeX math recognition
• Built for print documents: Not suitable for handwriting, complex tables, degraded scans, or scripts outside Latin/Cyrillic/Greek/Devanagari/Bengali/Chinese; Datalab offers Chandra model for these cases

LiteParse is an open-source PDF parser that extracts text with spatial positioning and bounding boxes entirely on your machine, running 2-5ms per page without cloud dependencies or LLMs. It handles PDFs, Word docs, spreadsheets, and images, with optional OCR for scanned content.

• Benchmarks show LiteParse outperforms free competitors (pdf-inspector, opendataloader, markitdown) across three major document parsing tests, scoring 0.389 on ParseBench vs 0.283 for the next best model-free tool.
• Speed comes from avoiding ML models — uses PDFium for text extraction, optional Tesseract or PaddleOCR for scanned pages, and spatial layout reconstruction via grid projection.
• Available in Python, Node.js, Rust, and browser (WASM) with CLI tools; includes worker pool mode for parallel parsing with per-document timeouts to prevent pipeline stalls.

Datalab’s 4 billion-parameter Chandra OCR 2 outperforms GPT-4o and Gemini across independent and multilingual benchmarks, handling complex layouts, math notation, flowcharts and 90 languages with state-of-the-art accuracy. It’s available under Apache 2.0 code with a modified OpenRAIL-M license for weights, runs locally via HuggingFace or vLLM, and doubles throughput over its predecessor.

• Chandra OCR 2 (4B params, open-weight) scored 85.9% on olmOCR vs GPT-4o's 69.9%, and beat Gemini/GPT-5 Mini on multilingual benchmarks, with huge gains on South Asian scripts (Kannada +42.6, Malayalam +46.2, Telugu +39.1).
• It processes full pages in one pass rather than splitting into blocks, giving it an edge on tables, nested headers, checkboxes, handwritten math, and flowcharts exported as Mermaid diagrams.
• Despite shrinking from 9B to 4B parameters, throughput doubled to ~2 pages/sec on an H100 while accuracy improved.
• Code is Apache 2.0 and installable via pip/Docker, but weights use a modified OpenRAIL-M license requiring a paid commercial license for larger companies.