Open Source C++ Text Processing Software

DocWire SDK, a standout C++20AI driven data processing tool, has received award from SourceForge and strong backing from Microsoft. It handles nearly 100 file types, empowering efficient text extraction, web data extraction, and document analysis. For businesses, the shift to DocWire SDK signifies a leap forward. It promises comprehensive document format support and the ability to extract valuable insights from email boxes, databases, and websites using cutting-edge AI. DocWire SDK aims to expand its capabilities, focusing on versatile data extraction, platform support, and seamless integration with various systems. DocWire SDK is dedicated to streamlining data processing, reducing development time and costs, and harnessing the potential of AI. Its advancements promise a superior experience compared to its predecessor, DocToText.

When translating becomes a game ! Text to translate can be graphically selected. Several dictionnaries can be sorted according to the context. A large choice of matching strategies is available. The OCR engine is tunable.

SemNotes is a semantic note taking tool for KDE4, built on top of Nepomuk-KDE. The tool is still under development, but it is already usable, provided that KDE4 is installed and the Nepomuk running.

OCR c++ library. Include: contour recognition; vectorisation; matrix letter feature recognition; auto page segmentation and detect rotation; SS3 ASM core; XML base; web-based GUI; 99,6% printed Unicode text recognition; letter base up to 1200 letters.

CRF decoder is the simplified version of CRF++, only for decoding the sequential data. It removes the training component and its correspondent codes from CRF++, which makes CRF decoder more reabable and understandable for freshman.

FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. ext classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool. The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

Machine translation engine based on a dependency grammar and XML interchange format. The Spanish-Basque (es-eu) translation is ready.