AIMC Topic: Handwriting

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Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks.

Computers in biology and medicine
Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-...

Analysis and evaluation of handwriting in patients with Parkinson's disease using kinematic, geometrical, and non-linear features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting a...

Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks.

Computational intelligence and neuroscience
In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanc...

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach.

IEEE journal of biomedical and health informatics
Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can...

Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO Memristive Spiking-Neuron.

Scientific reports
Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1...

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering pro...

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Neural networks : the official journal of the International Neural Network Society
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse bi...

An adaptive deep Q-learning strategy for handwritten digit recognition.

Neural networks : the official journal of the International Neural Network Society
Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further im...

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

Cognitive processing
In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Per...

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.

PloS one
We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evalu...