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Handwriting

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Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks.

Mathematical biosciences and engineering : MBE
Despite the increasing use of technology, handwriting has remained to date as an efficient means of communication. Certainly, handwriting is a critical motor skill for childrens cognitive development and academic success. This article presents a new ...

Development of the Ultralight Hybrid Pneumatic Artificial Muscle: Modelling and optimization.

PloS one
Pneumatic artificial muscles (PAMs) are one of the key technologies in soft robotics, and they enable actuation in mobile robots, in wearable devices and exoskeletons for assistive and rehabilitative purposes. While they recently showed relevant impr...

Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa).

PloS one
The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. This study takes an innovative approach to palaeography-the study of ancient handwriting-as a new entry point to access this scribal culture. One of the problems of pa...

A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO).

IEEE transactions on neural networks and learning systems
Gradient-based algorithms have been widely used in optimizing parameters of deep neural networks' (DNNs) architectures. However, the vanishing gradient remains as one of the common issues in the parameter optimization of such networks. To cope with t...

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN.

Sensors (Basel, Switzerland)
Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a nee...

Top interpretable neural network for handwriting identification.

Journal of forensic sciences
Machine learning (ML) has become one of the most promising tools in forensics, despite its dominant method of artificial neural networks (ANNs) suffering from the black-box problem. While forensic methodology demands explainability and evaluativity, ...

Restoring and attributing ancient texts using deep neural networks.

Nature
Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of past civilizations. However, over the centuries, many inscriptions have been dama...

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

Sensors (Basel, Switzerland)
For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire w...

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.

Scientific reports
Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural n...

Measuring Human Perception to Improve Handwritten Document Transcription.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we consider how to incorporate psychophysical measurements of human visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can reduce errors. A...