592 Open source projects that are alternatives of or similar to ocr-machine-learning

经典机器学习算法的极简实现

Programmatically find and read labels using Machine Learning

Animated Visualizations of Popular Machine Learning Algorithms

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

🔎📝 This is a module to make specifics OCRs at food products and nutritional tables.

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machine learning algorithms in Swift

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run a multitude of classifiers on you data and get an AUC report

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Naive Bayes classifier for use in Lua

Receipt parser application written in dart.

Complete toolbox for gamifying language learning

画 - Japanese OCR Dictionary

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Natural Language Processing using NLTK and Spacy

On-Device ID Card & Passport & Driver License Recognition SDK for Android

To identify the driver's drowsiness based on real-time camera image and image processing techniques. 졸음운전 감지 시스템. OpenCV

A translator for Linux, running at the background which wakes up with the translation of the last selected text on command.

naive bayes in php

A pytorch implemented classifier for Multiple-Label classification

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OCR detection implement with tensorflow v1.4

A License Classifier

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…

Vehicle-Rear: A New Dataset to Explore Feature Fusion For Vehicle Identification Using Convolutional Neural Networks

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Identifying numbers from bankcard, based on Deep Learning with Keras [China Software Cup 2019]

No description or website provided.

A highly scriptable automation system full of cool features. Automate everything with a little bit of Lua.

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OCR-D python tools

The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.

A simple, smart and efficient document scanner for Android

liblinear bindings for Go

NeuSpell: A Neural Spelling Correction Toolkit

This project is to convert PortraitFCN+ (by Xiaoyong Shen) from Matlab to Tensorflow, then refine the outputs from it (converted to a trimap) using KNN and ResNet, supervised by Richard Berwick.

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In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

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Papermerge RESTful backend structured as reusable Django app

This is a pytorch re-implementation of Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

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Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Machine Learning inference engine for Microcontrollers and Embedded devices