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Journal articles on the topic 'Offline Handwriting'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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Al Abodi, Jafaar, and Xue Li. "An effective approach to offline Arabic handwriting recognition." Computers & Electrical Engineering 40, no. 6 (August 2014): 1883–901. http://dx.doi.org/10.1016/j.compeleceng.2014.04.014.

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12

Plötz, Thomas, and Gernot A. Fink. "Markov models for offline handwriting recognition: a survey." International Journal on Document Analysis and Recognition (IJDAR) 12, no. 4 (October 31, 2009): 269–98. http://dx.doi.org/10.1007/s10032-009-0098-4.

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13

Mridha, M. F., Abu Quwsar Ohi, M. Ameer Ali, Mazedul Islam Emon, and Muhammad Mohsin Kabir. "BanglaWriting: A multi-purpose offline Bangla handwriting dataset." Data in Brief 34 (February 2021): 106633. http://dx.doi.org/10.1016/j.dib.2020.106633.

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Ajmire, P. E. "Offline Handwritten Devanagari Numeral Recognition Using Artificial Neural Network." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2016): 79. http://dx.doi.org/10.23956/ijarcsse.v7i8.27.

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Machine recognition of handwriting has been improving from last decay. The task of machine learning and recognition which also include reading handwriting is closely resembling human performance is still an open problem and also the central issue of an active field of research. Many researchers are working for fully automating the process of reading, understanding and interpretation of handwritten character. This research work proposes new approaches for extracting features in context of Handwritten Marathi numeral recognition. For classification technique Artificial Network is used. The overall accuracy of recognition of handwritten Devanagari numerals is 99.67% with SVM classifier, 99% with MLP and it is 98.13with GFF.

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15

TÍMÁR, GERGELY, KRISTÓF KARACS, and CSABA REKECZKY. "ANALOGIC PREPROCESSING AND SEGMENTATION ALGORITHMS FOR OFFLINE HANDWRITING RECOGNITION." Journal of Circuits, Systems and Computers 12, no. 06 (December 2003): 783–804. http://dx.doi.org/10.1142/s0218126603001185.

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This report describes analogic algorithms used in the preprocessing and segmentation phase of offline handwriting recognition tasks. A segmentation-based handwriting recognition approach is discussed, i.e., the system attempts to segment the words into their constituent letters. In order to improve their speed, the utilized CNN algorithms, whenever possible, use dynamic, wave front propagation-based methods instead of relying on morphologic operators were embedded into iterative algorithms. The system first locates the handwritten lines in the page image, then corrects their skew as necessary. It then searches for the words within the lines and corrects the skew at the word level as well. A novel trigger wave-based word segmentation algorithm is presented, which operates on the skeletons of words. Sample results of experiments conducted on a database of 25 handwritten pages along with suggestions for future development are presented.

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Jemni, Sana Khamekhem, Yousri Kessentini, and Slim Kanoun. "Improving Recurrent Neural Networks for Offline Arabic Handwriting Recognition by Combining Different Language Models." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (April 21, 2020): 2052007. http://dx.doi.org/10.1142/s0218001420520072.

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In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.

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17

Morera, Ángel, Ángel Sánchez, José Francisco Vélez, and Ana Belén Moreno. "Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks." Complexity 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/3891624.

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Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones. Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addressed before by other researchers. Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems. Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.

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18

Kef, Maamar, Leila Chergui, and Salim Chikhi. "A new large Arabic database for offline handwriting recognition." International Journal of Applied Pattern Recognition 1, no. 1 (2013): 81. http://dx.doi.org/10.1504/ijapr.2013.052342.

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19

Nopsuwanchai, R., A. Biem, and W. F. Clocksin. "Maximization of mutual information for offline Thai handwriting recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 8 (August 2006): 1347–51. http://dx.doi.org/10.1109/tpami.2006.167.

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20

Wong, L. C., and W. P. Loh. "Segregating Offline and Online Handwriting for Conditional Classification Analysis." IOP Conference Series: Materials Science and Engineering 530 (July 15, 2019): 012058. http://dx.doi.org/10.1088/1757-899x/530/1/012058.

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Sadri, Javad, Mohammad Reza Yeganehzad, and Javad Saghi. "A novel comprehensive database for offline Persian handwriting recognition." Pattern Recognition 60 (December 2016): 378–93. http://dx.doi.org/10.1016/j.patcog.2016.03.024.

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22

Srihari, Sargur N., Xuanshen Yang, and Gregory R. Ball. "Offline Chinese handwriting recognition: an assessment of current technology." Frontiers of Computer Science in China 1, no. 2 (May 2007): 137–55. http://dx.doi.org/10.1007/s11704-007-0015-2.

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23

Pasaribu, Novie Theresia Br, and M. Jimmy Hasugian. "Feature Extraction Comparison in Handwriting Recognition of Batak Toba Alphabet." IJITEE (International Journal of Information Technology and Electrical Engineering) 1, no. 3 (January 4, 2018): 86. http://dx.doi.org/10.22146/ijitee.31969.

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Offline handwriting recognition is one of the most prominent research topics due to its tremendous application and high variability as well. This paper covers the offline Batak Toba handwritten text recognition, from the noise removal, the process of feature extraction until the recognition by using several classifiers. Experiments show that elliptic fourier descriptor (EFD) is the most discriminative feature and Mahalanobis distance (MD) outperforms the two others classifier.

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Fitrianingsih, Fitrianingsih, Sarifuddin Madenda, Ernastuti Ernastuti, Suryarini Widodo, and Rodiah Rodiah. "Cursive Handwriting Segmentation using Ideal Distance Approach." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (October 1, 2017): 2863. http://dx.doi.org/10.11591/ijece.v7i5.pp2863-2872.

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Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting.

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El Moubtahij, Hicham, A. Halli, and Khalid Satori. "Arabic Handwriting Text Offline Recognition Using the HMM Toolkit (HTK)." International Review on Computers and Software (IRECOS) 9, no. 7 (July 31, 2014): 1214. http://dx.doi.org/10.15866/irecos.v9i7.2258.

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Sueiras, Jorge, Victoria Ruiz, Angel Sanchez, and Jose F. Velez. "Offline continuous handwriting recognition using sequence to sequence neural networks." Neurocomputing 289 (May 2018): 119–28. http://dx.doi.org/10.1016/j.neucom.2018.02.008.

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Asokan, Ayna, and Sreeleja N Unnithan. "Offline Recognition of Malayalam and Kannada Handwritten Documents Using Deep Learning." International Journal of Computer Communication and Informatics 3, no. 2 (October 30, 2021): 12–24. http://dx.doi.org/10.34256/ijcci2122.

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For a variety of reasons, handwritten text can be digitalized. It is used in a variety of government entities, including banks, post offices, and archaeological departments. Handwriting recognition, on the other hand, is a difficult task as everyone has a different writing style. There are essentially two methods for handwritten recognition: a holistic and an analytic approach. The previous methods of handwriting recognition are time- consuming. However, as deep neural networks have progressed, the approach has become more straightforward than previous methods. Furthermore, the bulk of existing solutions are limited to a single language. To recognise multilanguage handwritten manuscripts offline, this work employs an analytic approach. It describes how to convert Malayalam and Kannada handwritten manuscripts into editable text. Lines are separated from the input document first. After that, word segmentation is performed. Finally, each word is broken down into individual characters. An artificial neural network is utilised for feature extraction and classification. After that, the result is converted to a word document.

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LIWICKI, MARCUS, and HORST BUNKE. "HANDWRITING RECOGNITION OF WHITEBOARD NOTES — STUDYING THE INFLUENCE OF TRAINING SET SIZE AND TYPE." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 01 (February 2007): 83–98. http://dx.doi.org/10.1142/s0218001407005314.

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This paper presents a system for the recognition of online whiteboard notes. Notes written on a whiteboard is a new modality in handwriting recognition research that has received relatively little attention in the past. For the recognition we use an offline HMM-recognizer, which is supplemented with methods for processing the online data and generating offline images. The system consists of six main modules: online preprocessing, transformation of online to offline data, offline preprocessing, feature extraction, classification and post-processing. The recognition rate of our basic recognizer in a writer independent experiment is 59.5%. By applying state-of-the-art methods, such as optimizing the number of states and Gaussian components, and by including a language model we could achieve a statistically significant increase of the recognition rate to 64.3%. To further improve the system performance we increased the size of the training set. For that we investigated two different strategies. First, we used another existing database of offline handwritten text. Second, we used a recently collected whiteboard database, called the IAM-OnDB. By means of these strategies the recognition rate could be further increased up to 68.5%.

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Youssef, Nahla Ibrahim, and Nadia Abd-Alsabour. "A REVIEW ON ARABIC HANDWRITING RECOGNITION." Journal of Southwest Jiaotong University 57, no. 6 (December 30, 2022): 745–64. http://dx.doi.org/10.35741/issn.0258-2724.57.6.66.

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Handwriting recognition is considered a very hard area of research, especially for Arabic, because of its ligatures, cursive nature, diacritics, and overlapping. Although many studies have been conducted on Arabic recognition, this field still has many unsolved problems. This work aims to provide a comprehensive review of various strategies for handling Arabic handwriting recognition. Furthermore, it details handwriting recognition, general recognition, Arabic recognition, its characteristics, and the difficulties it faces. Additionally, we discuss online and offline Arabic recognition and other classifications of Arabic recognition methods. We also highlight efforts related to the Arabic datasets and the most important ones, such as the first online Quranic handwritten word dataset. Moreover, we address other efforts related to Arabic recognition that don't deal with the recognition process itself, such as estimating the dates of historical Arabic documents.

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Bisht, Mamta, and Richa Gupta. "Multiclass Recognition of Offline Handwritten Devanagari Characters using CNN." International Journal of Mathematical, Engineering and Management Sciences 5, no. 6 (December 1, 2020): 1429–39. http://dx.doi.org/10.33889/ijmems.2020.5.6.106.

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The handwriting style of every writer consists of variations, skewness and slanting nature and therefore, it is a stimulating task to recognise these handwritten documents. This article presents a study on various methods available in literature for Devanagari handwritten character recognition and performs its implementation using Convolutional neural network (CNN). Available methods are studied on different parameters and a tabular comparison is also presented which concludes superiority of CNN model in character recognition task. The proposed CNN model results in well acceptable accuracy using dropout and stochastic gradient descent (SGD) optimizer.

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Marti, U. V., and H. Bunke. "The IAM-database: an English sentence database for offline handwriting recognition." International Journal on Document Analysis and Recognition 5, no. 1 (November 1, 2002): 39–46. http://dx.doi.org/10.1007/s100320200071.

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张, 祥祥. "The Application of Hidden Markov Models in Offline Handwriting Digital Recognition." Computer Science and Application 08, no. 05 (2018): 702–8. http://dx.doi.org/10.12677/csa.2018.85078.

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Rabaev, Irina, Izadeen Alkoran, Odai Wattad, and Marina Litvak. "Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet." Sensors 22, no. 24 (December 9, 2022): 9650. http://dx.doi.org/10.3390/s22249650.

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This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.

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LAU, KAI KWONG, PONG CHI YUEN, and YUAN YAN TANG. "UNIVERSAL WRITING MODEL FOR RECOVERY OF WRITING SEQUENCE OF STATIC HANDWRITING IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 05 (August 2005): 603–30. http://dx.doi.org/10.1142/s0218001405004277.

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Online features have been proven to be more robust information for handwriting recognition than an offline static image due to dynamic aspects, such as the writing sequence of strokes. The estimation of temporal information from a static image becomes an important issue. This paper presents a new statistical method to reconstruct the writing order of a handwritten signature from a two-dimensional static image. The reconstruction process consists of two phases, namely the training phase and the testing phase. In the training phase, the writing order with other attributes, such as length and direction, are extracted and analyzed from a set of training online handwritten signatures. A Universal Writing Model (UWM), which consists of a set of distribution functions, is then constructed. In the testing phase, the UWM is applied to reconstruct the writing order of an offline signature. 300 offline signatures with ground truth are used for evaluation. Experimental results show that about one-eighth of the reconstructed writing sequences are the same as the actual writing sequences.

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VARGA, T., and H. BUNKE. "OFFLINE HANDWRITING RECOGNITION USING SYNTHETIC TRAINING DATA PRODUCED BY MEANS OF A GEOMETRICAL DISTORTION MODEL." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1285–302. http://dx.doi.org/10.1142/s0218001404003666.

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A perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. The goal of synthetic textline generation is to improve the performance of an offline cursive handwriting recognition system by providing it with additional training data. It can be expected that by adding synthetic training data the variability of the training set improves, which leads to a higher recognition rate. On the other hand, synthetic training data may bias a recognizer towards unnatural handwriting styles, which could lead to a deterioration of the recognition rate. In this paper the proposed perturbation model is evaluated under several experimental conditions, and it is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by a large number of different writers.

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Onuean, Athita, Uraiwan Buatoom, Thatsanee Charoenporn, Taehong Kim, and Hanmin Jung. "Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset." Applied Sciences 12, no. 8 (April 18, 2022): 4083. http://dx.doi.org/10.3390/app12084083.

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In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai handwriting image dataset named Burapha-TH. The dataset has 68 character classes, 10 digit classes, and 320 syllable classes. For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets that were then digitized using a 300 dpi scanner. De-skewing, detection box and segmentation algorithms were applied to the raw scans for image extraction. The experiment used different deep convolutional models with the proposed dataset. The result shows that the VGG-13 model (with batch normalization) achieved accuracy rates of 95.00%, 98.29%, and 96.16% on character, digit, and syllable classes, respectively. The Burapha-TH dataset, unlike all other known Thai handwriting datasets, retains existing noise, the white background, and all artifacts generated by scanning. This comprehensive, raw, and more realistic dataset will be helpful for a variety of research purposes in the future.

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Et. al., S. V. Kedar,. "Identifying Learning Disability Through Digital Handwriting Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 46–56. http://dx.doi.org/10.17762/turcomat.v12i1s.1557.

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Handwriting Analysis is described as a scientific study for the analysis of handwriting. It is a way of interpreting and ability to learn from peculiarities in handwriting. Offline handwriting analysis is a traditional approach that cannot be used efficiently for analysis. Online handwriting analysis, on the other hand, can utilize various aspects like pressure on the pen, timestamp and other factors which help in improving the effectiveness of analysis. Learning disabilities are neurological processing problems which can hamper the learning of the children. Dysgraphia is a learning disability that mainly affects a child’s handwriting and motor skills. It is found in 10 to 30% of school-aged children. Dysgraphia can be diagnosed by therapists based on children’s handwriting samples and manual evaluation techniques. This method is lengthy and inaccurate. In this work, automatic identification methods for and classification of dysgraphia in children in the age group 7 to 12 is described. The method performs analyzing of the child’s writing dynamics via blueprints of the pressure the pen puts on paper with the pen’s movements and orientation with the use of a standardized digital writing pad and machine learning algorithms. It basically has two phases, the training phase, and testing phase. In the training phase, handwriting samples of known results are given to the system. Then the model is built using some classifier, Random forest or Support Vector Machine. Once the model is trained, then in testing phase this model is used for classification of unknown samples to predict whether the subject has dysgraphia or not. This is then used to check the accuracy of the designed system.

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Amirgaliyev, Y., Mateus Mendes, K. Mukhtar, R. Jantayev, and Ch Kenchimov. "ResNet50+Transformer: kazakh offline handwritten text recognition." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 84, no. 2 (June 15, 2022): 11–24. http://dx.doi.org/10.47533/2020.1606-146x.150.

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Nowadays, due to the transition to digital data storage, there is a need to implement handwritten text recognition (HTR), which is an automatic translation of handwritten characters into a machine format. Handwriting recognition is complicated by the fact that there are many languages and it is possible to write the same character in different ways. In this regard, we conducted a study of a machine learning model for recognizing handwritten characters using databases of the Kazakh language. We trained the ResNet50 + Transformer deep learning model using two published databases of the Kazakh language: KOHTD and HKR. In the course of the study, these databases were studied on the component and qualitative sides with a comparison of the results of validation of the trained model. As a result, the KOHTD database showed results in the form of CER-9.46% and WER-20.18%, while the HKR database showed results in the form of CER-6.08% and WER-15.51%.

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Mohammed, Twana Latif, and Ahmed Abdullah Ahmed. "Offline Writer Recognition for Kurdish Handwritten Text Document Based on Proposed Codebook." UHD Journal of Science and Technology 5, no. 1 (March 31, 2021): 21–27. http://dx.doi.org/10.21928/uhdjst.v5n2y2021.pp21-27.

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Handwritten text recognition has been an ongoing attractive task to research in the field of document analysis and recognition with applications in handwriting forensics, paleography, document examination, and handwriting recognition. In the present research, an automatic method of writer recognition is presented using digitized images of unconstrained texts. Despite the increasing efforts by prior literature on the different methods used for the same purpose, such methods performance, particularly their accuracy, has not been promising, leaving plenty of room for improvements. This method made use of codebook-based writer characterization, with each writing sample represented by a group of computed features from a primary and secondary codebook. The writings were then represented through the computation of the probability of codebook patterns occurrence, and the probability distribution was employed for each writer’s characterization. Writer identification process involved comparing two writings through the computation of the distances between their respective probability distribution. The study carried out experiments to determine the performance of the implemented method in light of rates of identification with the help of standard datasets, namely, KRDOH and IAM, the former being the most current and largest Kurdish handwritten datasets with 1076 writers, and the latter being a dataset containing 650 writers. The outcome of the experiments was promising with a rate of identification of 94.3%, with the proposed method outperforming the state-of-the-art methods by 2–3%.

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Roy, Partha Pratim, Guoqiang Zhong, and Mohamed Cheriet. "Tandem hidden Markov models using deep belief networks for offline handwriting recognition." Frontiers of Information Technology & Electronic Engineering 18, no. 7 (July 2017): 978–88. http://dx.doi.org/10.1631/fitee.1600996.

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Maalej, Rania, and Monji Kherallah. "New MDLSTM-based designs with data augmentation for offline Arabic handwriting recognition." Multimedia Tools and Applications 81, no. 7 (February 14, 2022): 10243–60. http://dx.doi.org/10.1007/s11042-022-12339-8.

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Kaur, Ravneet. "Handwriting Recognition of Gurmukhi Script: A Survey of Online and Offline Techniques." International Journal of Computer Trends and Technology 49, no. 1 (July 25, 2017): 32–35. http://dx.doi.org/10.14445/22312803/ijctt-v49p106.

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Al-Maadeed, Somaya. "Text-Dependent Writer Identification for Arabic Handwriting." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/794106.

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This paper proposes a system for text-dependent writer identification based on Arabic handwriting. First, a database of words was assembled and used as a test base. Next, features vectors were extracted from writers' word images. Prior to the feature extraction process, normalization operations were applied to the word or text line under analysis. In this work, we studied the feature extraction and recognition operations of Arabic text on the identification rate of writers. Because there is no well-known database containing Arabic handwritten words for researchers to test, we have built a new database of offline Arabic handwriting text to be used by the writer identification research community. The database of Arabic handwritten words collected from 100 writers is intended to provide training and testing sets for Arabic writer identification research. We evaluated the performance of edge-based directional probability distributions as features, among other characteristics, in Arabic writer identification. Results suggest that longer Arabic words and phrases have higher impact on writer identification.

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Fitrianingsih, Fitrianingsih, Diana Tri Susetianingtias, Dody Pernadi, Eka Patriya, and Rini Arianty. "The Implementation of Artificial Neural Network (ANN) on Offline Cursive Handwriting Image Recognition." ILKOM Jurnal Ilmiah 14, no. 1 (April 30, 2022): 63–73. http://dx.doi.org/10.33096/ilkom.v14i1.1113.63-73.

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Khandelwal, Hemant, Sakshi Gupta, and Arihant Kumar Jai. "Review of Offline Handwriting Recognition Techniques in the fields of HCR and OCR." International Journal of Computer Trends and Technology 47, no. 3 (May 25, 2017): 161–64. http://dx.doi.org/10.14445/22312803/ijctt-v47p123.

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Ghanim, Taraggy M., Mahmoud I. Khalil, and Hazem M. Abbas. "Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition." IEEE Access 8 (2020): 95465–82. http://dx.doi.org/10.1109/access.2020.2994290.

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Gupta, Sheifali, Udit Jindal, Deepali Gupta, and Rupesh Gupta. "Offline Handwritten Gurumukhi Character Recognition System Using Convolution Neural Network." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4164–69. http://dx.doi.org/10.1166/jctn.2019.8497.

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A lot of literature is available on the recognition of handwriting on scripts other than Indians, but the number of articles related to Indian scripts recognition such as Gurumukhi are much less. Gurumukhi is a religion-specific language that ranks 14th frequently spoken language in all languages of the world. In Gurumukhi script, some characters are alike to each other which makes recognition task very difficult. Therefore this article presents a novel approach for Gurumukhi character. This article lays emphasis on convolutional neural networks (CNN), which intend to obtain the features of given data samples and then its mapping is being performed to the right observation. In this approach, a dataset has been prepared for 10 Gurumukhi characters. The proposed methodology obtains a recognition accuracy of 99.34% on Gurumukhi characters images without making use of any post-processing method.

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Dongre, Vikas J., and Vijay H. Mankar. "Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/871834.

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In handwritten character recognition, benchmark database plays an important role in evaluating the performance of various algorithms and the results obtained by various researchers. In Devnagari script, there is lack of such official benchmark. This paper focuses on the generation of offline benchmark database for Devnagari handwritten numerals and characters. The present work generated 5137 and 20305 isolated samples for numeral and character database, respectively, from 750 writers of all ages, sex, education, and profession. The offline sample images are stored in TIFF image format as it occupies less memory. Also, the data is presented in binary level so that memory requirement is further reduced. It will facilitate research on handwriting recognition of Devnagari script through free access to the researchers.

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OKAMOTO, MASAYOSHI, and KAZUHIKO YAMAMOTO. "ONLINE HANDWRITING CHARACTER RECOGNITION METHOD USING DIRECTIONAL AND DIRECTION-CHANGE FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 1041–59. http://dx.doi.org/10.1142/s0218001499000586.

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We propose a new online recognition method to recognize handwritten cursive-style Japanese characters correctly. Our method simultaneously uses both directional features, otherwise known as offline features, and direction-change features which we designed as online features. The direction-change features express where in the mesh and in which direction the character's coordinates change. These features express both written strokes in the pen-down state and unwritten imaginary strokes in the pen-up state. The recognition rate was improved by our method over the traditional method using only directional features.

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Maqqor, Ahlam, Akram Halli, Khalid Satori, and Hamid Tairi. "Offline Arabic Handwriting Recognition System Based on the Combination of Multiple Semi-Continuous HMMs." International Review on Computers and Software (IRECOS) 10, no. 7 (July 31, 2015): 677. http://dx.doi.org/10.15866/irecos.v10i7.6229.

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