Search Results for "structured text" - Page 13
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Showing 332 open source projects for "structured text"
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This project describes and implements a delimiter-based rule system that can be used to parse simple, structured-text "wiki" markup languages.
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This module provides a framework for adding advanced articles containing text (structured in sections) together with related images, links and/or additional information to the OpenCms (version 6) content management system.
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RSane Publisher allows a team of authors to easily maintain shared documents. Publisher speeds authoring of large structured documents, especially technical, business, and reference materials. Runs on Java, JBoss, and Tomcat.
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FramerD is a distributed semi-structured object database originally developed at MIT. It provides an internationalized Scheme-based scripting language, built-in text analysis tools, and special support for web scripting.
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High-performance software for information retrieval research. Emphasis on semi-structured text retrieval, especially for HTML and XML. The goal is to facilitate information retrieval research by providing an interchangable toolkit of functions.
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A shorthand alternative to XML. A set of software tools written in Java for dealing with text that is structured by indentation rather than with tags. The tools include a parser, an object representation, XPath evaluator, a schema validator and more.
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Chaperon is a LALR(1) parser, which parse structured text documents and generate XML documents as output. It includes a parser generator like yacc and a regex scaner like lex. As input use Chaperon a grammar written in XML.
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Amylase is a set of tools/libraries written in Java, which converts various structured text files such computer programs/DTD files into HTML or XML documents. The name, Amylase, comes from a word ML-ize, where ML stands for Markup Language as in XML.
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SystemSpecifyer is used to develop and maintain complex, well structured, systems specifications, in normal language. Using the System Matrix double matrix relation building mechanism specs can be tested for completeness, correctness and consistency.
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txt2xml is a simple Java library for parsing arbitrarily structured text streams into XML documents.
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A modular system for extracting and converting Python docstrings into useful structured formats like HTML, XML, and TeX. Project inactive. Development taken over by Docutils, http://docutils.sourceforge.net/.
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An implementation of Ted Nelson's ZigZag(tm) structure. ZigZag is a new type of programming platform for structured data.
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Stef is the "Structured Text Editor Framework". It is an extensible editor for working with hierarchically structured text (like XML, programming languages and other types of parseable text).
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A database builder/editor/viewer for maintaining RDB tables using the Linux-based Agenda VR3 PDA. Users can create and edit structured data records compatible with the RDB tab-delimited text format.
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Unlimited-OCR
Layout-aware OCR model for multilingual document understanding
Unlimited-OCR is Baidu’s open-source optical character recognition (OCR) model designed to accurately extract and understand text from complex documents, images, and multilingual content. Unlike traditional OCR systems that focus only on text detection and transcription, Unlimited-OCR combines advanced document parsing with language understanding, enabling it to recognize structured elements such as tables, formulas, charts, and mixed-layout documents while preserving their logical organization. ... -
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Qwen2.5-VL-3B-Instruct
Qwen2.5-VL-3B-Instruct: Multimodal model for chat, vision & video
Qwen2.5-VL-3B-Instruct is a 3.75 billion parameter multimodal model by Qwen, designed to handle complex vision-language tasks in both image and video formats. As part of the Qwen2.5 series, it supports image-text-to-text generation with capabilities like chart reading, object localization, and structured data extraction. The model can serve as an intelligent visual agent capable of interacting with digital interfaces and understanding long-form videos by dynamically sampling resolution and frame rate. It uses a SwiGLU and RMSNorm-enhanced ViT architecture and introduces mRoPE updates for robust temporal and spatial understanding. ... -
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translategemma-4b-it
Lightweight multimodal translation model for 55 languages
translategemma-4b-it is a lightweight, state-of-the-art open translation model from Google, built on the Gemma 3 family and optimized for high-quality multilingual translation across 55 languages. It supports both text-to-text translation and image-to-text extraction with translation, enabling workflows such as OCR-style translation of signs, documents, and screenshots. With a compact ~5B parameter footprint and BF16 support, the model is designed to run efficiently on laptops, desktops, and private cloud infrastructure, making advanced translation accessible without heavy hardware requirements. ... -
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DiffusionGemma
NVFP4 DiffusionGemma model for fast multimodal text generation
DiffusionGemma 26B A4B IT NVFP4 is NVIDIA’s Model Optimizer quantized release of Google DeepMind’s DiffusionGemma 26B A4B IT model. It is an open-weights multimodal generative model that processes text, images, and video inputs to produce text output through discrete diffusion. Built on the Gemma 4 26B A4B Mixture-of-Experts architecture, it has 25.2B total parameters and 3.8B active parameters, balancing capability with efficient inference. Its diffusion-based generation produces tokens in parallel 256-token blocks, enabling very high-speed output, with reported generation above 1,100 tokens per second on NVIDIA Hopper H100 in FP8. ... -
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Qwen2.5-VL-7B-Instruct
Multimodal 7B model for image, video, and text understanding tasks
Qwen2.5-VL-7B-Instruct is a multimodal vision-language model developed by the Qwen team, designed to handle text, images, and long videos with high precision. Fine-tuned from Qwen2.5-VL, this 7-billion-parameter model can interpret visual content such as charts, documents, and user interfaces, as well as recognize common objects. It supports complex tasks like visual question answering, localization with bounding boxes, and structured output generation from documents. ... -
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Gemma 4
Google’s flagship dense multimodal model for coding and reasoning
Gemma 4 is Google DeepMind’s flagship dense open-weight multimodal model, designed for high-end reasoning, coding, agentic workflows, and multimodal understanding. The model contains approximately 30.7B parameters and supports text and image inputs with text generation output, while also processing video as image-frame sequences. Built as the most capable model in the Gemma 4 family, it combines strong reasoning performance with a large 256K-token context window and configurable thinking modes. Gemma 4 31B supports native function calling, structured outputs, and more than 140 languages, making it suitable for enterprise assistants, coding agents, document analysis, and multilingual applications. ... -
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Qwen2.5-14B-Instruct
Powerful 14B LLM with strong instruction and long-text handling
Qwen2.5-14B-Instruct is a powerful instruction-tuned language model developed by the Qwen team, based on the Qwen2.5 architecture. It features 14.7 billion parameters and is optimized for tasks like dialogue, long-form generation, and structured output. The model supports context lengths up to 128K tokens and can generate up to 8K tokens, making it suitable for long-context applications. It demonstrates improved performance in coding, mathematics, and multilingual understanding across over... -
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layoutlm-base-uncased
Multimodal Transformer for document image understanding and layout
layoutlm-base-uncased is a multimodal transformer model developed by Microsoft for document image understanding tasks. It incorporates both text and layout (position) features to effectively process structured documents like forms, invoices, and receipts. This base version has 113 million parameters and is pre-trained on 11 million documents from the IIT-CDIP dataset. LayoutLM enables better performance in tasks where the spatial arrangement of text plays a crucial role. The model uses a standard BERT-like architecture but enriches input with 2D positional embeddings. ... -
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This is a cross-platform C++ library that provides an intuitive and quick way to deal with tree structured configuration text files. It is also very small, making it easy and comfortable to include in any project.
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Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require logical reasoning, analysis, or structured thinking. Despite its modest size, the model is designed for edge deployment and can run locally, fitting in ~16 GB of VRAM in BF16 or under 8 GB of RAM/VRAM when quantized. ...
