On Using Entropy for Enhancing Handwriting Preprocessing

Holzinger, Andreas; Stocker, Christof; Peischl, Bernhard; Simonic, Klaus Martin

doi:10.3390/e14112324

Cited by 25 publications

(6 citation statements)

References 42 publications

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“…This intern enhances the speed and accuracy. Preprocessing usually consists of steps like binarisation, normalisation, smoothing and de-noising (Holzinger et al, 2012). Feature extraction is the process of finding a set of parameters that precisely and uniquely define the shape of the character.…”

Section: Related Workmentioning

confidence: 99%

A modified GA classifier for offline Tamil handwritten character recognition

Nelson¹,

Rao

2017

IJAPR

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In this paper, we proposed a classifier for Tamil handwritten character recognition using skeletonisation and modified GA to improve the recognition results of offline Tamil handwritten characters. The skeletonised character image is traversed from one endpoint to the other in an order, and based on the path of traversal, skeletonisation is explored to generate feature vector. The operations of conventional GA are modified to allow variable string length of chromosomes in GA. Fitness function is computed by integrating the classification capacity of the string metric Levenshtein distance which measures the dissimilarities between two strings. The experimental results on offline Tamil dataset demonstrates that the proposed classifier can automatically minimise the rate of misclassification and also provide better performance compared to GA with the fixed length chromosome. Our algorithm withstands even noisy data. Its comparison with other approaches is also substantiated and results proved that the proposed algorithm exhibits high accuracy in between 85% to 95%.

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Section: Related Workmentioning

confidence: 99%

A modified GA classifier for offline Tamil handwritten character recognition

Nelson¹,

Rao

2017

IJAPR

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“…[46], [51], [52], [53], [54]. There are sheer endless possibilities of applications to solve various problems in different areas; here only a few recent examples [55], [56], [57], [58], [59], [60]. A tool to test methods, aiming at finding the most appropriate solution for a specific problem can be found here [61].…”

Section: Knowledge Discovery In a Nutshellmentioning

confidence: 99%

Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together?

Holzinger

2013

Lecture Notes in Computer Science

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107

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A major challenge in our networked world is the increasing amount of data, which require efficient and user-friendly solutions. A timely example is the biomedical domain: the trend towards personalized medicine has resulted in a sheer mass of the generated (-omics) data. In the life sciences domain, most data models are characterized by complexity, which makes manual analysis very time-consuming and frequently practically impossible. Computational methods may help; however, we must acknowledge that the problem-solving knowledge is located in the human mind and-not in machines. A strategic aim to find solutions for data intensive problems could lay in the combination of two areas, which bring ideal preconditions: Human-Computer Interaction (HCI) and Knowledge Discovery (KDD). HCI deals with questions of human perception, cognition, intelligence, decision-making and interactive techniques of visualization, so it centers mainly on supervised methods. KDD deals mainly with questions of machine intelligence and data mining, in particular with the development of scalable algorithms for finding previously unknown relationships in data, thus centers on automatic computational methods. A proverb attributed perhaps incorrectly to Albert Einstein illustrates this perfectly: "Computers are incredibly fast, accurate, but stupid. Humans are incredibly slow, inaccurate, but brilliant. Together they may be powerful beyond imagination". Consequently, a novel approach is to combine HCI & KDD in order to enhance human intelligence by computational intelligence.

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“…In comparison with traditional handwriting [8], stylus writing is difficult because of the limitations of stylus pens and their support systems [9]. Some studies have examined the usability of a stylus with small devices to overcome limitations such as limited space [10,11].…”

Section: Introductionmentioning

confidence: 99%

Usability of the Stylus Pen in Mobile Electronic Documentation

Park¹,

Pobil

Kwon

2015

Electronics

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Stylus pens are often used with mobile information devices. However, few studies have examined the stylus' simple movements because the technical expertise to support documentation with stylus pens has not been developed. This study examined the usability of stylus pens in authentic documentation tasks, including three main tasks (sentence, table, and paragraph making) with two types of styluses (touchsmart stylus and mobile stylus) and a traditional pen. The statistical results showed that participants preferred the traditional pen in all criteria. Because of inconvenient hand movements, the mobile stylus was the least preferred on every task. Mobility does not provide any advantage in using the stylus. In addition, the study also found inconvenient hand support using a stylus and different feedback between a stylus and a traditional pen.

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On Using Entropy for Enhancing Handwriting Preprocessing

Cited by 25 publications

References 42 publications

A modified GA classifier for offline Tamil handwritten character recognition

A modified GA classifier for offline Tamil handwritten character recognition

Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together?

Usability of the Stylus Pen in Mobile Electronic Documentation

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