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SwiftOCR is a native Swift library designed for extracting text and alphanumeric characters from images. It functions as a neural network text recognizer that identifies characters and strings from visual data.
The main features of garnele007/swiftocr are: Alphanumeric Code Recognition, Custom Font Recognition, Custom Model Training, Image-to-Text Extraction, OCR Model Customizers, OCR Language Training, OCR Model Trainings, Neural Networks.
Open-source alternatives to garnele007/swiftocr include: nmac427/swiftocr — SwiftOCR is a Swift library for performing optical character recognition and text extraction from images. It functions… ub-mannheim/tesseract — Tesseract is an optical character recognition engine and tool designed to convert printed or handwritten text from… tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision… brainjs/brain.js — Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the… cazala/synaptic — Synaptic is a JavaScript neural network library used for building, training, and executing neural networks in Node.js… apache/mxnet — This project is a deep learning framework designed for constructing, training, and deploying neural networks across…
SwiftOCR is a Swift library for performing optical character recognition and text extraction from images. It functions as a neural network text recognizer and OCR model trainer designed for iOS and macOS applications. The project provides tools for custom font training, allowing users to teach neural networks to recognize specific typography or unique character sets by processing custom datasets. This enables the system to identify short alphanumeric sequences based on specific target character mappings. The library includes an image-preprocessing pipeline to clean and transform visual data
Tesseract is an optical character recognition engine and tool designed to convert printed or handwritten text from images into machine-readable digital text. It functions as a multilingual text extractor and a document digitization pipeline that transforms scanned images into structured digital formats. The project includes a framework for training custom scripts and language-specific models, allowing the engine to recognize new languages or unique fonts through custom training data. Its capabilities cover automated text extraction, digital archive digitization, and the export of recognized
Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the browser or Node.js. It provides implementations for several network types, including feedforward networks, recurrent neural networks for time series forecasting, and autoencoders for data compression and denoising. The library features WebGL-based GPU acceleration to increase the speed of neural network computations on the graphics processor. It also includes a visualization tool that generates SVG images to represent the topology and layers of a feedforward network. The framewo
Synaptic is a JavaScript neural network library used for building, training, and executing neural networks in Node.js and the browser. It provides a framework for constructing architecture-free neural network topologies, a backpropagation training engine for weight optimization, and a toolkit for implementing recurrent neural network frameworks. The library enables the design of custom first or second order network architectures without predefined constraints. It supports a variety of specialized models, including Long Short-Term Memory networks, Hopfield networks, Liquid State Machines, and