Xberg is a single engine for detecting, reading, OCR’ing, and extracting text, tables, metadata, and structured data from over 100 document formats and 115 file extensions. It offers transcription, embeddings, layout reconstruction, schema-driven JSON extraction, and code intelligence via language bindings in Rust, Python, Go, Java, and more. You can run it as a library, CLI, REST API, or MCP server with configurable Cargo features.

• One Rust-core engine handles 101 document formats (115 extensions) plus 371 programming languages for code intelligence
• Ships as 15 language bindings (Python, Go, Java, Ruby, PHP, Elixir, C#, TypeScript, etc.) plus CLI, REST API, and MCP server
• Combines OCR (Tesseract/PaddleOCR/VLM), layout reconstruction (PP-DocLayout-V3, RT-DETR), and table extraction (TATR, SLANet) with schema-driven JSON output via local or hosted LLMs
• Runs GPU-free with multi-GB streaming support and safeguards against zip bombs and excessive nesting/compression

Mistral OCR 4 extracts text from PDFs, DOCs and more while also returning bounding boxes, block types and per-word confidence. It supports 170 languages, runs in a single container for self-hosted deployments, and outperforms rivals on human and automated benchmarks.

• Mistral OCR 4 doesn't just extract text—it returns bounding boxes, block types (titles, tables, equations, signatures), and per-word confidence scores, and can be self-hosted in a single container for data-sovereignty needs
• Beat every tested competitor in human evaluations across 600+ documents/12+ languages with a 72% average win rate, and scored 85.20 on OlmOCRBench and 93.07 on OmniDocBench
• Roughly 4x faster than some enterprise OCR providers, and 8x cheaper with 17x lower latency than leading agentic document parsers on finance datasets with charts/figures
• Priced at $4 per 1,000 pages via API ($2 with batch discount) or $5 per 1,000 pages through the no-code Document AI interface

Unlimited-OCR is an open-source OCR framework from Baidu that extends Deepseek-OCR to handle single images, multi-page documents, and PDFs. It provides Hugging Face transformer models, PyMuPDF conversion, and an OpenAI-compatible SGLang server for streaming or batch inference. The repo includes setup instructions, example scripts, and configuration for GPU-accelerated parsing.

• Extends Deepseek-OCR to handle not just single images but multi-page documents and PDFs, with a 32,768-token context window for long-horizon parsing
• Includes an OpenAI-compatible SGLang server supporting streaming inference, plus a batch script that auto-launches the server and processes image/PDF folders concurrently
• PDF handling pipeline renders pages at 300 DPI via PyMuPDF, then feeds them through model.infer_multi with a no-repeat-ngram constraint to avoid repetitive output
• Offers two preset image configs ("gundam" with crop mode at 1024/640, "base" at fixed 1024x1024) for trading off resolution and processing mode

This daily roundup highlights new AI tools, models, and research—from Mistral’s OCR 4 and ByteDance’s Seedance 2.5 video generator to Anthropic’s Claude Tag and IBM’s CUGA agent harness. It also covers security deep dives on indirect prompt injection, industry moves like OpenAI’s bidirectional voice and US‐Meta AI reviews, plus several open-source releases.

• Mistral OCR 4 handles 170 languages and runs 4x faster than rivals, especially on low-resource scripts
• ByteDance's Seedance 2.5 generates 30-second 4K videos from a prompt plus up to 50 reference images, clips, or audio files
• Airbyte Agents cuts tool calls by 40%, token use by 80%, and multi-source query costs by 90% by indexing business data instead of querying APIs live
• OpenAI's new Bidirectional Voice Mode can hold real-time back-and-forth conversations and even sing or beatbox within copyright limits

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.

Chandra OCR 2, a 4 billion-parameter model from Datalab, outperforms GPT-4o and Gemini on AllenAI’s olmOCR benchmark and a 90-language test while halving the model size. It preserves layout, reads complex tables and math notation, converts diagrams to Mermaid, and runs at two pages per second on an NVIDIA H100. The code is Apache 2.0 but the model weights use an OpenRAIL-M license with commercial restrictions.

• Chandra OCR 2 scores 85.9% on olmOCR vs GPT-4o's 69.9%, while cutting model size from 9B to 4B parameters and hitting ~2 pages/sec on an H100
• Multilingual performance beats Gemini 2.5 Flash and GPT-5 Mini, with 40-46 point gains on scripts like Kannada, Malayalam and Telugu over Chandra 1
• Weights carry an OpenRAIL-M license requiring a paid commercial license once a company exceeds $2M in funding or revenue, despite Apache 2.0 code
• Handwriting recognition remains weak, dropping to ~50.4% accuracy on complex forms despite strong printed-text and table/math handling

A new open-source OCR model outperformed all major commercial tools on standard text and handwriting tests. It accurately transcribed a 1913 handwritten letter by Ramanujan, preserving layout, math notation, and faint ink details.

• New open-source OCR model (Vision Transformers + glyph-aware training) beat commercial tools like ABBYY and Google Cloud Vision on IFOCR, MLT, and ICDAR benchmarks, with word-error-rates below 1.5%—30-50% better than competitors
• Successfully transcribed a faded 1913 handwritten Ramanujan letter, correctly capturing layout, math notation, and faint ink strokes
• Trained on millions of scanned pages including medieval manuscripts and dense handwritten notes
• Released under Apache license with CPU/GPU deployment scripts, enabling free text extraction from receipts, legal documents, and historical archives without per-page fees

A powerful CLI tool and browser extension that generates fast summaries from URLs, files, and media, including YouTube videos and podcasts. It features a Chrome Side Panel and Firefox Sidebar, supports various media types, and provides advanced functionalities like OCR and transcript extraction. The tool can be installed via npm or Homebrew, with options for local and paid model endpoints.

• Summarize is a CLI + Chrome/Firefox extension that turns any URL, file, YouTube video, or podcast into a fast summary, with a Chrome Side Panel for chat/history.
• Version 0.10.0 adds transcript-first media handling (using published transcripts or falling back to Whisper), OCR, and timestamped slide extraction from videos.
• A background daemon service offloads heavy extraction work so the browser stays fast and unburdened.
• Installable via npm or Homebrew, with configurable output (Markdown/JSON), summary length, and support for local or paid model endpoints.