Search Results for "example"

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. EasyOCR is a python module for extracting text from image. It is a general OCR that can read both natural scene text and dense text in document. We are currently supporting 80+ languages and expanding. Second-generation models: multiple times smaller size, multiple times faster inference, additional characters and comparable accuracy to the first...

...It is designed to extract text from images, PDFs, and scanned documents, and integrates with multimodal capabilities that understand layout, context, and visual elements beyond raw character recognition. The system treats OCR not simply as “read the text” but as “understand what the text is doing in the image”—for example distinguishing captions from body text, interpreting tables, or recognizing handwritten versus printed words. It supports local deployment, enabling organizations concerned about privacy or latency to run the pipeline on-premises rather than send sensitive documents to third-party cloud services. The codebase is written in Python with a focus on modularity: you can swap preprocessing, recognition, and post-processing components as needed for custom workflows.

Scribe.js is a JavaScript library that provides Optical Character Recognition (OCR) and text extraction capabilities for both images and PDF documents, aimed at developers who want to build OCR features directly into their applications. The library can take image files (such as PNG or JPEG) and recognize the text they contain, and it can also extract text from PDF files that either already contain text or are image-based scans, using modern web standards and WebAssembly under the hood. In...

myOCR brain chung is a small highly accurate OCR char recognition and Ann neural network brain example written in freebasic .

...After extracting the data you can compare players and view the data as a large sortable table. You can also export the results as text file or render the table as an image (for sharing with your guild for example). This project is still in a early development stage, please read the Wiki (see menu above) for details about what is working and what is not. There are still many features on the todo list for future release. Please consider to support the project by making a donation to the project by clicking the Donation link in the menu!

...Main object is a sparse bit string (Bits) that efficiently scales up to 2^63 bits normally compressed as forest has duplicated substrings. Bits objects support reading bit, byte, short, int, or long (Java primitives) at any bit index in 64 bit range. Example: instead of building a class to hold a header and then data, represent all of that as Bits, subranges of them, and ints for sizes of its parts. Expansion ability for other kinds of compression, since Bits is a Java interface. Main functions on bits are substring, concat, number of 0 or 1 bits, and number of bits (size). All those operations can be done millions of times per second regardless of size because the AVL forest reuses existing branches recursively. ...

DictionaryReader is a research project; the aim of which is to create software that can automatically import data from mixed-script dictionaries. (For example, a Tibetan-English dictionary.)