Recognizing arbitrarily connected and superimposed handwritten numerals in intangible writing interfaces

Chiang, Cheng‐Chin; Wang, Ren-Hong; Chen, Bo-Ruei

doi:10.1016/j.patcog.2016.07.018

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…The work states that an accuracy of 99.76% and 95.22% has been obtained for printed and handwritten numerals, respectively. Segmenting and recognizing arbitrarily connected and superimposed handwritten numeral recognition in onestroke finger gestures has been a problem and a solution to this has been proposed by Chiang et al, [3]. The method has two phases, key numeral spotting (KNS) phase and recognition by concatenation (RBC) phase.…”

Section: -Literature Surveymentioning

confidence: 99%

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Ramesh¹,

B²,

Bhat³

et al. 2021

JIST

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Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used.

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Section: -Literature Surveymentioning

confidence: 99%

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Ramesh¹,

B²,

Bhat³

et al. 2021

JIST

View full text Add to dashboard Cite

show abstract

“…Recognition methodologies are using the captured hand or arm position trajectories as features and Neuronal Networks (NNs) or Hidden Markov Models (HMMs) (see Section 4 ) as classifiers to extract human-readable text. Another approach uses video-based hand tracking to recognise characters in one-stroke finger gestures [ 99 ]. Chang et al [ 100 ] proposed a framework for mid-air handwriting recognition which is intended for use with wearable egocentric cameras.…”

Section: Applications and Contextsmentioning

confidence: 99%

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Bachmann

Weichert

Rinkenauer

2018

Sensors

127

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Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

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Person Authentication by Air-Writing Using 3D Sensor and Time Order Stroke Context

Chiu

Hsieh

Lai

et al. 2018

Lecture Notes in Computer Science

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Recognizing arbitrarily connected and superimposed handwritten numerals in intangible writing interfaces

Cited by 13 publications

References 36 publications

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Person Authentication by Air-Writing Using 3D Sensor and Time Order Stroke Context

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