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Recognition of Handwriting from Electromyography  [PDF]
Michael Linderman, Mikhail A. Lebedev, Joseph S. Erlichman
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0006791
Abstract: Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear.
Off-Line Arabic Handwriting Character Recognition Using Word Segmentation  [PDF]
Manal A. Abdullah,Lulwah M. Al-Harigy,Hanadi H. Al-Fraidi
Computer Science , 2012,
Abstract: The ultimate aim of handwriting recognition is to make computers able to read and/or authenticate human written texts, with a performance comparable to or even better than that of humans. Reading means that the computer is given a piece of handwriting and it provides the electronic transcription of that (e.g. in ASCII format). Two types of handwriting: on-line and offline. The most important purpose of off-line handwriting recognition is in protection systems and authentication. Arabic Handwriting scripts are much more complicated in comparison to Latin scripts. This paper introduces a simple and novel methodology to authenticate Arabic handwriting characters. Reaching our aim, we built our own character database. The research methodology depends on two stages: The first is character extraction where preprocessing the word and then apply segmentation process to obtain the character. The second is the character recognition by matching the characters comprising the word with the letters in the database. Our results ensure character recognition with 81%. We eliminate FAR by using similarity percent between 45-55%. Our research is coded using MATLAB.
An Empirical Evaluation of Off-line Arabic Handwriting And Printed Characters Recognition System  [PDF]
Firoj Parwej
International Journal of Computer Science Issues , 2012,
Abstract: Handwriting recognition is a challenging task for many real-world applications such as document authentication, form processing, historical documents. This paper focuses on the comparative study on off-line handwriting recognition system and Printed Characters by taking Arabic handwriting. The off-line Handwriting Recognition methods for Arabic words which being often used among then across the Middle East and North Africa people. In this paper we are proposing off-line Arabic handwriting and printed characters and the language used by the majority of the Middle East. We are using discrete Hidden Markov Models (HMM) for Arabic handwriting and printed characters for the final recognition. In this paper after preprocessing step the characters are auto-segmented using a recursive algorithm as sequences of connected neighbors along lines and curves and Arabic words are first pre-classified into one of known character groups, based on the structural properties of the text line. The proposed system was trained and tested Arabic character images. The Arabic characters were written by the different people on a preformatted paper and the method recognizes the Arabic handwriting in print style format. A comparative Experimental result has shown 93.40% recognition rate for the Arabic handwriting and 97.30% recognition rate for the Arabic printed characters.
Arabic Handwriting Word Recognition Based on a Hybrid HMM/ANN Approach
Narima Zermi,Messaoud Ramdani,Mouldi Bedda
International Journal of Soft Computing , 2012,
Abstract: This study describes a hidden Markov model using a grapheme neural networks approach designed to recognize off-line unconstrained Arabic handwritten words. After pre-processing, a word image is segmented into characters or pseudo-characters called graphemes and represented by a sequence of observations. Each observation consists of a set of global and local features that reflect the geometrical and topological properties of a grapheme accompanied with information concerning its affiliation to one of five predefined groups. Within its group, the classification of a grapheme is done by a neural network trained with fuzzy class memberships rather than crisp class memberships as desired outputs because it results in more useful grapheme recognition modules for handwritten word recognition. The experimental results on a test database are presented to demonstrate the reliability of this study.
De-noising Approach for Online Handwriting Character Recognition Based on Mathematical Morphology

SUN Yan,LIU Han-meng,RUI Jian-wu,WU Jian,

计算机科学 , 2009,
Abstract: There are lots of various noises when users are writing characters on the tablet.It's significant to eliminate these noises in order to make these characters be recognized accurately.According to the features of online handwriting characters,we analyzed all sorts of noises generated during writing on the tablet.By applying erosion,dilation,thinning operations of the mathematical morphology into pre-processing of online handwriting recognition,a de-noising approach was proposed in this article.Using appropri...
CITlab ARGUS for Arabic Handwriting  [PDF]
Gundram Leifert,Roger Labahn,Tobias Strau?
Computer Science , 2014,
Abstract: In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and dictionary lookup, our ARGUS software completed this task with an error rate of 26.27% in its primary setup.
On Using Entropy for Enhancing Handwriting Preprocessing  [PDF]
Andreas Holzinger,Christof Stocker,Bernhard Peischl,Klaus-Martin Simonic
Entropy , 2012, DOI: 10.3390/e14112324
Abstract: Handwriting is an important modality for Human-Computer Interaction. For medical professionals, handwriting is (still) the preferred natural method of documentation. Handwriting recognition has long been a primary research area in Computer Science. With the tremendous ubiquity of smartphones, along with the renaissance of the stylus, handwriting recognition has become a new impetus. However, recognition rates are still not 100% perfect, and researchers still are constantly improving handwriting algorithms. In this paper we evaluate the performance of entropy based slant- and skew-correction, and compare the results to other methods. We selected 3700 words of 23 writers out of the Unipen-ICROW-03 benchmark set, which we annotated with their associated error angles by hand. Our results show that the entropy-based slant correction method outperforms a window based approach with an average precision of ±6.02° for the entropy-based method, compared with the ±7.85° for the alternative. On the other hand, the entropy-based skew correction yields a lower average precision of ±2:86°, compared with the average precision of ±2.13° for the alternative LSM based approach.
An Open Source Testing Tool for Evaluating Handwriting Input Methods  [PDF]
Liquan Qiu,Lianwen Jin,Ruifen Dai,Yuxiang Zhang,Lei Li
Computer Science , 2015,
Abstract: This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods. The tool consists of two modules, namely the PC and Android mobile client. The PC client reads handwritten samples in the computer, and transfers them individually to the Android client in accordance with the socket communication protocol. After the Android client receives the data, it simulates the handwriting on screen of client device, and triggers the corresponding handwriting recognition method. The recognition accuracy is recorded by the Android client. We present the design principles and describe the implementation of the test platform. We construct several test datasets for evaluating different handwriting recognition systems, and conduct an objective and comprehensive test using six Chinese handwriting input methods with five datasets. The test results for the recognition accuracy are then compared and analyzed.
Writer Identification for Chinese Handwriting
Wong Yee Leng,Siti Mariyam Shamsuddin
International Journal of Advances in Soft Computing and Its Applications , 2010,
Abstract: Chinese handwriting identification has become a hot research inpattern recognition and image processing. In this paper, we presentoverview of relevant papers from the previous related studies until tothe recent publications regarding to the Chinese HandwritingIdentification. The strength, weaknesses, accurateness andcomparison of well known approaches are reviewed, summarizedand documented. This paper provides broad spectrum of patternrecognition technology in assisting writer identification tasks, whichare at the forefront of forensic and biometrics based on identificationapplication.
Stroke-Based Cursive Character Recognition  [PDF]
K. C. Santosh,E. Iwata
Computer Science , 2013,
Abstract: Human eye can see and read what is written or displayed either in natural handwriting or in printed format. The same work in case the machine does is called handwriting recognition. Handwriting recognition can be broken down into two categories: off-line and on-line. ...
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