Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

A comparison of supervised classification methods for a statistical set of features: Application: Amazigh OCR

Record type:

paper
Creator:
NabKarKha
Publisher:
IEEE
Host:

Visit

doi.org

Tasks

computer visionoptical character recognition

Languages

AmazighBerber

Similar

A robust statistical set of features for Amazigh handwritten charactersA comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddingsSynthesis of supervised classification algorithm using intelligent and statistical toolsSeasonal forecasting of hydrological drought in the Limpopo Basin a comparison of statistical methodsYsneAtigui/Amazigh-OCRThe development of a fine grained class set for Amazigh POS tagging

A robust statistical set of features for Amazigh handwritten characters

A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings

Many speech processing tasks involve measuring the acoustic similarity between speech segments. Acou

Synthesis of supervised classification algorithm using intelligent and statistical tools

A fundamental task in detecting foreground objects in both static and dynamic scenes is to take th

Seasonal forecasting of hydrological drought in the Limpopo Basin a comparison of statistical methods

The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of

YsneAtigui/Amazigh-OCR

Tifinagh OCR system using CRNN (Convolutional Recurrent Neural Network) with VGG16-BN backbone, base

The development of a fine grained class set for Amazigh POS tagging