Relevant bibliographies by topics / Offline Handwriting

Academic literature on the topic 'Offline Handwriting'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Offline Handwriting"

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Kumar, J., and A. Roy. "DograNet – a comprehensive offline dogra handwriting character dataset." Journal of Physics: Conference Series 2251, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2251/1/012008.

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Abstract Handwritten Text Recognition is an important area of research because of growing demand to process and convert a huge data and information available in handwritten form to Digital form. The digital data instead of handwritten form can prove to be highly useful in different fields. Handwritten text recognition plays an important role in applications involved in, postal services, banks for cheque processing, searching of information and organization dealing with such applications. In text recognition application dataset of the specified script is required for training purpose. Datasets of the different languages could be found online but dataset of dogra script characters is still not available. This paper presents a Dogra handwriting character dataset which contains around 38690 character images etc grouped in 73 character classes extracted from 530 one-page handwritings of 265 individuals of having variable age, sex, qualification, location. The dogra character dataset would be freely accessible by scholars and researchers which could also be used for further recognition improvement and updating with more characters and word, Identification of writer, dogra word segmentation. Dogra dataset could also be used for extracting variation of handwriting according to age and gender.
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HE, ZHENYU, XINGE YOU, YUAN YAN TANG, BIN FANG, and JIANWEI DU. "HANDWRITING-BASED PERSONAL IDENTIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 02 (March 2006): 209–25. http://dx.doi.org/10.1142/s0218001406004612.

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Handwriting-based personal identification, which is also called handwriting-based writer identification, is an active research topic in pattern recognition. Despite continuous effort, offline handwriting-based writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting image. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is unavailable for offline writer identification. In this paper, we present a novel wavelet-based Generalized Gaussian Density (GGD) method for offline writer identification. Compared with the 2-D Gabor model, which is currently widely acknowledged as a good method for offline handwriting identification, GGD method not only achieves a better identification accuracy but also greatly reduces the elapsed time on calculation in our experiments.
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CHA, SUNG-HYUK, CHARLES C. TAPPERT, MICHAEL GIBBONS, and YI-MIN CHEE. "AUTOMATIC DETECTION OF HANDWRITING FORGERY USING A FRACTAL NUMBER ESTIMATE OF WRINKLINESS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1361–71. http://dx.doi.org/10.1142/s0218001404003642.

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We investigate the detection of handwriting forged by novices. To facilitate document examination it is important to develop an automated system to identify forgeries, or at least to identify those handwritings that are likely to be forged. Because forgers often carefully copy or trace genuine handwriting, we hypothesize that good forgeries — those that retain the shape and size of genuine writing — are usually written more slowly and are therefore wrinklier (less smooth) than genuine writing. From online handwriting samples we find that the writing speed of the good forgeries is significantly slower than that of the genuine writings. From corresponding offline samples we find that the wrinkliness of the good forgeries is significantly greater than that of the genuine writings, showing that this feature can help identify candidate forgeries from scanned documents. Using a total of eight handwriting distance features, including the wrinkliness feature, we train a neural network to achieved 89% accuracy on detecting forged handwriting on test samples from ten writers.
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Lorigo, L. M., and V. Govindaraju. "Offline Arabic handwriting recognition: a survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 5 (May 2006): 712–24. http://dx.doi.org/10.1109/tpami.2006.102.

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Steinherz, T., D. Doermann, E. Rivlin, and N. Intrator. "Offline Loop Investigation for Handwriting Analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 2 (February 2009): 193–209. http://dx.doi.org/10.1109/tpami.2008.68.

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KALERA, MEENAKSHI K., SARGUR SRIHARI, and AIHUA XU. "OFFLINE SIGNATURE VERIFICATION AND IDENTIFICATION USING DISTANCE STATISTICS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1339–60. http://dx.doi.org/10.1142/s0218001404003630.

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This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.
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Putri, Farica Perdana, and Adhi Kusnadi. "Pengenalan Tulisan Tangan Offline Dengan Algoritma Generalized Hough Transform dan Backpropagation." Jurnal ULTIMA Computing 10, no. 1 (July 10, 2018): 5–12. http://dx.doi.org/10.31937/sk.v10i1.890.

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Offline handwriting recognition is a technique used to recognize handwriting in paper document which converting it to digital form. Each handwriting has a unique style and shape that can be used to identify the owner. This research aims to develop a method to recognize the digital data handwriting. The method combines two algorithms; the first is Generalized Hough Transform in feature extraction process to detect arbitrary objects on the image; the second algorithm is Backpropagation to train the neural network based on feature values from feature extraction process. Artificial Neural Network (ANN) is used to improve the accuracy of the recognition system. The experiments are performed by using 100 handwriting images of 10 different people. The number of hidden units is defined through experiment to obtain optimal neural network. The experiment result shows that the recognition accuracy is up to 80%. Index Terms—Artificial Neural Network, Backrpopagation, Generalized Hough Transform, Offline handwiritng recognition
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Rosalina and R. B. Wahyu. "Offline Handwriting Recognition Using Feedforward Neural Network." International Journal of Information Technology and Computer Science 9, no. 9 (September 8, 2017): 11–17. http://dx.doi.org/10.5815/ijitcs.2017.09.02.

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Alabodi, Jafaar, and Xue Li. "An Effective Approach to Offline Arabic Handwriting Recognition." International Journal of Artificial Intelligence & Applications 4, no. 6 (November 30, 2013): 1–16. http://dx.doi.org/10.5121/ijaia.2013.4601.

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Haraty, R. A., and H. M. El-Zabadani. "Abjad Hawwaz: An Offline Arabic Handwriting Recognition System." International Journal of Computers and Applications 27, no. 3 (January 2005): 178–89. http://dx.doi.org/10.1080/1206212x.2005.11441767.

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Dissertations / Theses on the topic "Offline Handwriting"

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Kennard, Douglas J. "Warping-Based Approach to Offline Handwriting Recognition." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3991.

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An enormous amount of the historical record is currently trapped in non-indexed handwritten format. Even after being scanned into images, only a minute fraction of the existing records can be manually transcribed / indexed with reasonable amounts of time and cost. Although progress continues to be made with automatic handwriting recognition (HR), it is not yet good enough to replace manual transcription or indexing. Much of the recent HR work has focused on incremental improvements to methods based on Hidden Markov Models (HMMs) and other similar probabilistic approaches. In this dissertation we present a fundamentally new approach to HR based on 2-D geometric warping of word images. The results of our experimentation indicate that our approach is significantly more accurate than an existing whole-word approach used for word-spotting, and may also be better than HMM-based HR approaches. Since it is a completely new method, we also believe there is potential for improvement and future work that builds on this approach. In addition, we demonstrate that the approach can be used effectively in the related application domain of signature verification and forgery detection.
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Günter, Simon. "Multiple classifier systems in offline cursive handwriting recognition." [S.l.] : [s.n.], 2004. http://www.stub.unibe.ch/download/eldiss/04guenter_s.pdf.

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Chherawala, Youssouf. "Feature design and lexicon reduction for efficient offline handwriting recognition." Mémoire, École de technologie supérieure, 2014. http://espace.etsmtl.ca/1273/1/CHHERAWALA_Youssouf.pdf.

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Cette thèse établit un cadre de travail de reconnaissance de formes pour les systèmes de reconnaissance de mots hors-ligne. Elle se concentre sur les caractéristiques de l’image, car elles ont une grande influence sur les performances de reconnaissance. En particulier, nous considérons deux aspects complémentaires de l’impact des caractéristiques: la réduction du lexique et la reconnaissance elle-même. Le premier aspect, la réduction du lexique, consiste à concevoir un classifieur faible qui fournit en sortie un ensemble d’hypothèses de mots à partir d’une image de mot. Son objectif principal est de réduire le temps de calcul de la reconnaissance tout en maintenant (voire améliorant) le taux de reconnaissance. Le deuxième aspect est le système de reconnaissance proprement dit. Plusieurs caractéristiques existent dans la littérature, issues de différents domaines de recherche, mais il n’existe pas de consensus sur les pistes les plus prometteuses. L’objectif de cette thèse est d’améliorer notre compréhension des caractéristiques pertinentes pour construire des systèmes de reconnaissance encore plus performants. À cette fin, nous avons abordé deux problèmes spécifiques: 1) la conception de caractéristiques pour la réduction du lexique (appliquée à l’écriture arabe), et 2) l’évaluation de caractéristiques pour la reconnaissance de l’écriture manuscrite cursive (appliquée à l’écriture latine et arabe). Contrairement à l’écriture latine, la problématique de réduction du lexique est peu abordée pour l’écriture arabe. Les méthodes existantes utilisent certaines caractéristiques fondamentales des mots arabes telles que le nombre de sous-mots et les signes diacritiques, mais ignorent totalement la forme des sous-mots. Par conséquent, notre premier objectif est de créer une méthode de réduction du lexique basée sur la forme des sous-mots. Notre approche utilise l’indexation de formes, où la forme d’un sous-mot requête est comparée à une base de données étiquetée de sous-mots échantillons. Pour une comparaison efficace avec un faible temps de calcul, nous avons proposé le vecteur de signature topologique pondéré (W-TSV), où la forme du sous-mot est modélisée par un graphe acyclique orienté (DAG) pondéré, à partir duquel le vecteur W-TSV est extrait pour l’indexation. La principale contribution de ce travail est d’élargir le cadre existant du vecteur de signature topologique (TSV) aux DAGs pondérés et de proposer une approche d’indexation de formes pour la réduction du lexique. Cette approche est performante pour la réduction d’un lexique composé de sous-mots arabes. Néanmoins, ses performances restent modestes pour les mots arabes. Compte tenu des résultats de notre premier travail sur la réduction du lexique de mots arabe, nous proposons de construire un nouvel index pour de meilleures performances au niveau du mot. La forme des sous-mots, ainsi que leur nombre et celui des signes diacritiques sont des éléments importants de la forme du mot arabe. Nous proposons donc le descripteur de mot arabe (AWD) qui intègre toutes les composantes mentionnées ci-dessus. Il est construit en deux étapes. Tout d’abord, un descripteur de structure (SD) est calculé pour chaque composante connexe (CC) d’une image de mots. Il décrit la forme de la CC en utilisant le modèle de sac-de-mots, où chaque mot visuel représente une structure locale particulière. Ensuite, l’AWD est formé par la concaténation des SDs en utilisant une heuristique efficace, qui différencie implicitement les sous-mots des signes diacritiques. Dans le contexte de la réduction du lexique, l’AWD est utilisé pour indexer une base de données référence. La principale contribution de ce travail est la conception de l’AWD, qui intègre les caractéristiques de bas niveau (structure de la forme du sous-mot) et les informations symboliques (nombre de sous-mots et de signes diacritiques) en un seul descripteur. La méthode proposée possède un faible temps de calcul et elle est facile à implémenter. Elle fournit de meilleures performances pour la réduction du lexique sur deux bases de données d’écriture arabe, à savoir la base de données de sous-mots Ibn Sina et la base de données de mots IFN/ENIT. La dernière partie de cette thèse se concentre sur les caractéristiques visuelles pour la reconnaissance de mots. Un grand nombre de caractéristiques existent dans la littérature, chacune d’elles étant motivées par différents domaines, tels que la reconnaissance des formes, la vision par ordinateur ou l’apprentissage machine. Identifier les approches les plus prometteuses servirait à améliorer la conception de la prochaine génération de caractéristiques. Néanmoins, comme elles sont fondées sur des concepts différents, il est difficile de les comparer de manière théorique, des outils empiriques sont donc nécessaires. Par conséquent, le dernier objectif de la thèse est de fournir une méthode d’évaluation de caractéristiques en fonction de leur force et complémentarité. Un modèle de combinaison a été conçu à cet effet, dans lequel chaque caractéristique est évaluée au travers d’un système référence de reconnaissance, basée sur les réseaux de neurones récurrents. Plus précisément, chaque caractéristique est représentée par un agent, qui est une instance du système de reconnaissance entraînée à partir de cette caractéristique. Les décisions de tous les agents sont combinées en utilisant un vote pondéré. Les poids sont optimisés conjointement au cours d’une phase d’entraînement, afin d’augmenter le vote pondéré de la véritable étiquette de chaque mot. Par conséquent, les poids reflètent la force et la complémentarité des agents et de leurs caractéristiques pour la tâche donnée. Enfin, les poids sont convertis en scores numériques attribués aux caractéristiques, qui sont faciles à interpréter sous ce modèle de combinaison. Au meilleur de notre connaissance, c’est la première méthode d’évaluation de caractéristiques capable de quantifier l’importance de chaque caractéristique, au lieu d’établir un classement basé sur le taux de reconnaissance. Cinq caractéristiques de l’état de l’art ont été testées et nos résultats offrent une perspective intéressante pour la conception de futures caractéristiques.
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Elzobi, Moftah M. [Verfasser]. "Unconstrained recognition of offline Arabic handwriting using generative and discriminative classification models / Moftah M. Elzobi." Magdeburg : Universitätsbibliothek, 2017. http://d-nb.info/1135662185/34.

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Beránek, Jan. "Verifikace rukopisu a podpisu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237258.

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This paper concerns methods of verification of person's signature and handwriting. Some of commonly used techniques are resumed and described with related literature being referred. Next aim of this work is design and implementation of a simple handwriting verification application. Application is based on edge detection and comparison of a set of structural and statistical features. As a support classification tool a SVM classifier of the LIBSVM software is employed. The Application is written in C language using OpenCV graphics library. Testing and training set was extracted from samples found in the IAM Handwriting Database. Application was created and tested in the Windows XP operating system.
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Morillot, Olivier. "Reconnaissance de textes manuscrits par modèles de Markov cachés et réseaux de neurones récurrents : application à l'écriture latine et arabe." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0002.

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La reconnaissance d’écriture manuscrite est une composante essentielle de l’analyse de document. Une tendance actuelle de ce domaine est de passer de la reconnaissance de mots isolés à celle d’une séquence de mots. Notre travail consiste donc à proposer un système de reconnaissance de lignes de texte sans segmentation explicite de la ligne en mots. Afin de construire un modèle performant, nous intervenons à plusieurs niveaux du système de reconnaissance. Tout d’abord, nous introduisons deux méthodes de prétraitement originales : un nettoyage des images de lignes de texte et une correction locale de la ligne de base. Ensuite, nous construisons un modèle de langage optimisé pour la reconnaissance de courriers manuscrits. Puis nous proposons deux systèmes de reconnaissance à l’état de l’art fondés sur les HMM (Hidden Markov Models) contextuels et les réseaux de neurones récurrents BLSTM (Bi-directional LongShort-Term Memory). Nous optimisons nos systèmes afin de proposer une comparaison de ces deux approches. Nos systèmes sont évalués sur l’écriture cursive latine et arabe et ont été soumis à deux compétitions internationales de reconnaissance d’écriture. Enfin, enperspective de notre travail, nous présentons une stratégie de reconnaissance pour certaines chaînes de caractères hors-vocabulaire
Handwriting recognition is an essential component of document analysis. One of the popular trends is to go from isolated word to word sequence recognition. Our work aims to propose a text-line recognition system without explicit word segmentation. In order to build an efficient model, we intervene at different levels of the recognition system. First of all, we introduce two new preprocessing techniques : a cleaning and a local baseline correction for text-lines. Then, a language model is built and optimized for handwritten mails. Afterwards, we propose two state-of-the-art recognition systems based on contextual HMMs (Hidden Markov Models) and recurrent neural networks BLSTM (Bi-directional Long Short-Term Memory). We optimize our systems in order to give a comparison of those two approaches. Our systems are evaluated on arabic and latin cursive handwritings and have been submitted to two international handwriting recognition competitions. At last, we introduce a strategy for some out-of-vocabulary character strings recognition, as a prospect of future work
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Book chapters on the topic "Offline Handwriting"

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Rafique, Aftab, and M. Ishtiaq. "UOHTD: Urdu Offline Handwritten Text Dataset." In Frontiers in Handwriting Recognition, 498–511. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_34.

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Ghadhban, Haitham Qutaiba, Muhaini Othman, Noor Azah Samsudin, Mohd Norasri Bin Ismail, and Mustafa Raad Hammoodi. "Survey of Offline Arabic Handwriting Word Recognition." In Advances in Intelligent Systems and Computing, 358–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36056-6_34.

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Lin, Zihao, Jinrong Li, Fan Yang, Shuangping Huang, Xu Yang, Jianmin Lin, and Ming Yang. "Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offline Handwritten Mathematical Expression Recognition." In Frontiers in Handwriting Recognition, 213–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_15.

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Maalej, Rania, and Monji Kherallah. "Maxout into MDLSTM for Offline Arabic Handwriting Recognition." In Neural Information Processing, 534–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36718-3_45.

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Maalej, Rania, and Monji Kherallah. "ReLU to Enhance MDLSTM for Offline Arabic Handwriting Recognition." In Advances in Intelligent Systems and Computing, 386–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49342-4_37.

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Graves, Alex. "Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks." In Guide to OCR for Arabic Scripts, 297–313. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4072-6_12.

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Sharma, Annapurna, Rahul Ambati, and Dinesh Babu Jayagopi. "Towards Faster Offline Handwriting Recognition Using Temporal Convolution Networks." In Communications in Computer and Information Science, 344–54. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8697-2_32.

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Bezerra, Byron Leite Dantas, Cleber Zanchettin, and Vinícius Braga de Andrade. "A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition." In Artificial Neural Networks and Machine Learning – ICANN 2012, 246–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_31.

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Siegmund, Dirk, Tina Ebert, and Naser Damer. "Combining Low-Level Features of Offline Questionnaires for Handwriting Identification." In Lecture Notes in Computer Science, 46–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41501-7_6.

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Almodfer, Rolla, Shengwu Xiong, Mohammed Mudhsh, and Pengfei Duan. "Multi-column Deep Neural Network for Offline Arabic Handwriting Recognition." In Artificial Neural Networks and Machine Learning – ICANN 2017, 260–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_30.

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Conference papers on the topic "Offline Handwriting"

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Kennard, Douglas J., William A. Barrett, and Thomas W. Sederberg. "Word Warping for Offline Handwriting Recognition." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.271.

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Burnah, Tavish, and George L. Rudolph. "Offline Handwriting Recognition Pipeline Testing Tool." In 2020 Intermountain Engineering, Technology and Computing (IETC). IEEE, 2020. http://dx.doi.org/10.1109/ietc47856.2020.9249169.

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Cermeno, Eduardo, Silvana Mallor, and Juan Alberto Siguenza. "Offline handwriting segmentation for writer identification." In 2014 International Symposium on Biometrics and Security Technologies (ISBAST). IEEE, 2014. http://dx.doi.org/10.1109/isbast.2014.7013086.

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Lutf, Mohammed, Xinge You, and Hong Li. "Offline Arabic Handwriting Identification Using Language Diacritics." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.471.

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Natarajan, Prem, Krishna Subramanian, Anurag Bhardwaj, and Rohit Prasad. "Stochastic Segment Modeling for Offline Handwriting Recognition." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.278.

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Liu, Cheng-Lin, Fei Yin, Da-Han Wang, and Qiu-Feng Wang. "CASIA Online and Offline Chinese Handwriting Databases." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.17.

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Jemni, Sana Khamekhem, Yousri Kessentini, Slim Kanoun, and Jean-Marc Ogier. "Offline Arabic Handwriting Recognition Using BLSTMs Combination." In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2018. http://dx.doi.org/10.1109/das.2018.54.

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Fan, Daoerji, Guanglai Gao, and Huijuan Wu. "Sub-Word Based Mongolian Offline Handwriting Recognition." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00048.

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Qiao, Yu, Jianzhuang Liu, and Xiaoou Tang. "Offline Signature Verification Using Online Handwriting Registration." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383263.

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Chen, Jin, Huaigu Cao, Rohit Prasad, Anurag Bhardwaj, and Prem Natarajan. "Gabor features for offline Arabic handwriting recognition." In the 8th IAPR International Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1815330.1815337.

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