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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.
Handwriting Recognition  [PDF]
Jayati Ghosh Dastidar,Surabhi Sarkar,Rick Punyadyuti Sinha,Kasturi Basu
Computer Science , 2015,
Abstract: This paper describes the method to recognize offline handwritten characters. A robust algorithm for handwriting segmentation is described here with the help of which individual characters can be segmented from a selected word from a paragraph of handwritten text image which is given as input.
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.
Co-adaptation in a Handwriting Recognition System  [PDF]
Sunsern Cheamanunkul,Yoav Freund
Computer Science , 2014,
Abstract: Handwriting is a natural and versatile method for human-computer interaction, especially on small mobile devices such as smart phones. However, as handwriting varies significantly from person to person, it is difficult to design handwriting recognizers that perform well for all users. A natural solution is to use machine learning to adapt the recognizer to the user. One complicating factor is that, as the computer adapts to the user, the user also adapts to the computer and probably changes their handwriting. This paper investigates the dynamics of co-adaptation, a process in which both the computer and the user are adapting their behaviors in order to improve the speed and accuracy of the communication through handwriting. We devised an information-theoretic framework for quantifying the efficiency of a handwriting system where the system includes both the user and the computer. Using this framework, we analyzed data collected from an adaptive handwriting recognition system and characterized the impact of machine adaptation and of human adaptation. We found that both machine adaptation and human adaptation have significant impact on the input rate and must be considered together in order to improve the efficiency of the system as a whole.
Large Vocabulary Arabic Online Handwriting Recognition System  [PDF]
Ibrahim Abdelaziz,Sherif Abdou,Hassanin Al-Barhamtoshy
Computer Science , 2014,
Abstract: Arabic handwriting is a consonantal and cursive writing. The analysis of Arabic script is further complicated due to obligatory dots/strokes that are placed above or below most letters and usually written delayed in order. Due to ambiguities and diversities of writing styles, recognition systems are generally based on a set of possible words called lexicon. When the lexicon is small, recognition accuracy is more important as the recognition time is minimal. On the other hand, recognition speed as well as the accuracy are both critical when handling large lexicons. Arabic is rich in morphology and syntax which makes its lexicon large. Therefore, a practical online handwriting recognition system should be able to handle a large lexicon with reasonable performance in terms of both accuracy and time. In this paper, we introduce a fully-fledged Hidden Markov Model (HMM) based system for Arabic online handwriting recognition that provides solutions for most of the difficulties inherent in recognizing the Arabic script. A new preprocessing technique for handling the delayed strokes is introduced. We use advanced modeling techniques for building our recognition system from the training data to provide more detailed representation for the differences between the writing units, minimize the variances between writers in the training data and have a better representation for the features space. System results are enhanced using an additional post-processing step with a higher order language model and cross-word HMM models. The system performance is evaluated using two different databases covering small and large lexicons. Our system outperforms the state-of-art systems for the small lexicon database. Furthermore, it shows promising results (accuracy and time) when supporting large lexicon with the possibility for adapting the models for specific writers to get even better results.
A Comprehensive Survey on On-line Handwriting Recognition Technology and its Real Application to the Nepalese Natural Handwriting  [PDF]
Santosh KC,Cholwich Nattee
Kathmandu University Journal of Science, Engineering and Technology , 2009, DOI: 10.3126/kuset.v5i1.2845
Abstract: Handwriting Recognition Technology has been improving much under the purview of pattern recognition and image processing since a few decades. This paper focuses on the comprehensive survey on on-line handwriting recognition system along with the real application by taking Nepali natural handwriting (a real example of one of the cursive handwritings). The survey mainly includes pre-processing, feature vector and similarity measures in between the non-linear 2D sequences of coordinates, and their effective applications. A very highlighting topic "Dynamic Time Warping Algorithm'' (DTW) is introduced, which has been popular in determining the distance between two non-linear sequences ranging from handwriting to speech recognition. Besides these contemporary research issues/areas, stroke number and order free Nepalese natural handwritten recognition system is presented in the second step. Writing one's own style brings unevenness in writing units, which is the most difficult part to classify. Writing units reveal number, shape, size, order of stroke, and speed in writing. Variation in the number of strokes, their order, shapes and sizes, tilting angles and similarities among characters from one another are the important factors, which are to be considered in classification for Nepali. This paper utilizes structural properties of those alphanumeric characters, which have variable writing units. It uses a string of pen tip's positions and tangent angles of every consecutive point as a feature vector sequence of a stroke. We constructed a prototype recognizer that uses the DTW algorithm to align handwritten strokes with stored strokes' templates and determine their similarity. Separate system is trained for original and preprocessed writing samples and achieved recognition rates of 85.87% and 88.59% respectively. This introduces novel real time handwriting recognition on Nepalese alphanumeric characters, which are independent of number of strokes, as well as their order. Key Words : Handwriting Recognition System; Pre-processing; Feature Vector; Dynamic Time Warping; Agglomerating Hierarchical Clustering; Nepali. DOI: 10.3126/kuset.v5i1.2845 Kathmandu University Journal of Science, Engineering and Technology Vol.5, No.1, January 2009, pp 31-55
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.
Offline Arabic Handwriting Recognition Using Artificial Neural Network  [PDF]
A. A Zaidan,B. B Zaidan,Hamid. A. Jalab,Hamdan. O. Alanazi,Rami Alnaqeib
Computer Science , 2010,
Abstract: The ambition of a character recognition system is to transform a text document typed on paper into a digital format that can be manipulated by word processor software Unlike other languages, Arabic has unique features, while other language doesn't have, from this language these are seven or eight language such as ordo, jewie and Persian writing, Arabic has twenty eight letters, each of which can be linked in three different ways or separated depending on the case. The difficulty of the Arabic handwriting recognition is that, the accuracy of the character recognition which affects on the accuracy of the word recognition, in additional there is also two or three from for each character, the suggested solution by using artificial neural network can solve the problem and overcome the difficulty of Arabic handwriting recognition.
AltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database  [PDF]
Ibrahim Abdelaziz,Sherif Abdou
Computer Science , 2014,
Abstract: Arabic is a semitic language characterized by a complex and rich morphology. The exceptional degree of ambiguity in the writing system, the rich morphology, and the highly complex word formation process of roots and patterns all contribute to making computational approaches to Arabic very challenging. As a result, a practical handwriting recognition system should support large vocabulary to provide a high coverage and use the context information for disambiguation. Several research efforts have been devoted for building online Arabic handwriting recognition systems. Most of these methods are either using their small private test data sets or a standard database with limited lexicon and coverage. A large scale handwriting database is an essential resource that can advance the research of online handwriting recognition. Currently, there is no online Arabic handwriting database with large lexicon, high coverage, large number of writers and training/testing data. In this paper, we introduce AltecOnDB, a large scale online Arabic handwriting database. AltecOnDB has 98% coverage of all the possible PAWS of the Arabic language. The collected samples are complete sentences that include digits and punctuation marks. The collected data is available on sentence, word and character levels, hence, high-level linguistic models can be used for performance improvements. Data is collected from more than 1000 writers with different backgrounds, genders and ages. Annotation and verification tools are developed to facilitate the annotation and verification phases. We built an elementary recognition system to test our database and show the existing difficulties when handling a large vocabulary and dealing with large amounts of styles variations in the collected data.
De-noising Approach for Online Handwriting Character Recognition Based on Mathematical Morphology
基于数学形态学的联机手写字符识别去噪方法

SUN Yan,LIU Han-meng,RUI Jian-wu,WU Jian,
孙嫣
,刘瀚猛,芮建武,吴健

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...
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