A pattern recognition method of mathematical graphs

Takagi, Noboru

doi:10.1109/icsmc.2010.5642470

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Finally, it is interesting to compare the touching model with some material models such as a tactile graph [2] and a graph made by wire. As far as planar graphs are concerned, both models can represent any graph as it is.…”

Section: B Graph-type Modelsmentioning

confidence: 99%

On a Learning-Science System Supporting Visually Impaired Students via the Haptic Device

Suzuki

Araki

Kawai

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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We have proposed a system, referred to as HALS, which supports visually impaired students in learning science and mathematics by using a haptic device [1]. This article describes the second stage towards HALS. The basic idea of the system is, by taking good advantage of the haptic device, to provide them with haptic perceptions of scientific things such as 3D objects and motions of a body and, through the experience, to enable them to have images of these things in their mind. We expect that such an image of natural things promotes better understanding of science. In this work, we realize partially the above ideas and develop several models which produce virtual things visually handicapped students can perceive by means of the device. Some models produce planar and spatial geometrical objects and others do moving bodies. The efficiency of these models were examined by blind undergraduate students. We finally address merits and demerits of our system, and clarify the points to be improved.

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Section: B Graph-type Modelsmentioning

confidence: 99%

On a Learning-Science System Supporting Visually Impaired Students via the Haptic Device

Suzuki

Araki

Kawai

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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“…In [3], a pattern classification method of mathematical figures with broken lines has been proposed. The transformation of mathematical figures into tactile graphics with the help of pattern recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Math Accessibility for Blind People in Society Using Machine Learning

Shinde¹,

Alagirisamy²,

Wagh³

et al. 2022

ECS Trans.

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Math plays a crucial role in each and every sector belonging to human beings in society. Sometimes, it is very difficult to recognize math equations and symbols due to variation in writing, change in stroke, touching symbols, and many more. The blind people get very low success in math recognition as compared to character and digit recognition. It is needed to develop blind math application in which various math equations and symbols have to be recognized. The proposed system uses a machine learning approach to identify the various math equations and symbols by extracting various statistical and complex features with well-known classifier viz. support vector machine, neural network, and K-nearest neighbor. The math documents have to be scanned and recognized. Finally, a text to speech converter has been made to get the contents of math documents for blind people in society. The proposed system will be helpful for blind math applications and it will not affect the health of blind people in terms of stress on the eye to recognize so the health can be maintained and documents can be read.The implemented system can be used by blind people to read the math documents, digits on a bank check, calculate the valuation of currency, etc.

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“…For example, several types of software that can convert selected visual information into text form and then convey the data as synthesized speech or Braille, are already commercially available. Tactile graphics or pictures can be understood more easily by visually impaired persons, but it is sometimes difficult for visually impaired students to recognize mathematical figures correctly when using tactile devices [3]. The primary issue is that congenitally visually impaired persons (defined as persons who are born without sight or who lose it soon after birth) are unable to benefit from visual experiences or imagery.…”

Section: Introductionmentioning

confidence: 99%

Contour pattern recognition through auditory labels of freeman chain codes for people with visual impairments

Onishi

Ono

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Tactile devices are commonly used for pattern recognition learning by people with visual impairments. However, it is often difficult for such persons to recognize the detailed and mathematical shape of objects when using such devices. The primary difficulty is gaining a sufficiently accurate understanding of the shape of an object to permit its correct reconstruction based on tactile device feedback. Furthermore, people with visual impairments have less opportunity to discern visual images and must therefore apply their own sense of understanding to the shape of an object as gained from the tactile experiences. Thus, they are often not sure if it reflects the actual shape. To solve this problem, we propose a pattern recognition technique utilizing auditory labels based on a contour chain code. Using a Freeman chain, object shapes are represented as a sequence of steps, each in one of eight directions; each step is designated by an integer from 0 to 7. Our proposed method takes advantage of Freeman chain codes in order to present the detailed shape of objects. This report describes our proposed method and presents experimental results that verify its effectiveness in support of mathematical education efforts for visually impaired persons.

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A pattern recognition method of mathematical graphs

Cited by 7 publications

References 8 publications

On a Learning-Science System Supporting Visually Impaired Students via the Haptic Device

On a Learning-Science System Supporting Visually Impaired Students via the Haptic Device

Math Accessibility for Blind People in Society Using Machine Learning

Contour pattern recognition through auditory labels of freeman chain codes for people with visual impairments

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