We further investigate the problem of recognizing handwritten mathematical expressions, which we also chose for our CS221 final project [3]. Being able to change handwritten expressions into LATEX has applications for consumers and academics. While large amounts of work have been done for digit and character recognition [2] [10], much less progress has been made surrounding handwritten expressi...

Letter recognition and handwritten processing is one of the major and open problems in Artificial Intelligent (AI) domain. This study introduces a method based on statistical and geometrical techniques to recognize handwritten digits and letters. These techniques use the fuzzy logic to create the vector curves. Inputs are online digits or letters and outputs are two arrays of slope and curvatur...

In this paper, we discuss the challenges of sketch recognition accuracy and automation of the Clock Drawing Test (CDT). Sketch recognition in the context of the CDT is a complex problem due to the lack of knowledge of the preference bias among the sketches drawn by neuro-atypical patients. However, machine learning provides a viable solution to detect measurable patterns among sketches drawn in...

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the pres...

background and aim: digits are suitable materials for hearing assessment of wide range of patients including children and adults with various language abilities. single monosyllabic 1-10 digits in triplet formant have been used for measuring speech recognition threshold in signal-to-noise ratio. the purpose of this study was to determine signal-to-noise ratio (snr) needed for 50 percent correct...

This paper describes a modular class$cation system for handwritten digit recognition based on the elastic net model. We use ten separate elastic nets to capture different features in the ten classes of handwritten digits and represent an input sample from the activations in each net by population decoding. Compared with traditional neural networks based discriminant classifiers, our scheme feat...

With handwritten digit recognition being an established and significant problem that is facing computer vision and pattern recognition, there has been a great deal of research work that has been undertaken in this area. It is not a trivial task because of the big variation that exists in the writing styles that have been found in the available data. Therefore both, the features and the classifi...

Recognition of connected handwritten digit strings is a challenging task due mainly to two problems: poor character segmentation and unreliable isolated character recognition. In this paper, we first present a rational B-spline representation of digit templates based on Pixel-to-Boundary Distance (PBD) maps. We then present a neural network approach to extract B-spline PBD templates and an evol...