Journal articles: 'Devanagari online handwritten characters'

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Ghosh, Rajib, and Prabhat Kumar. "SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition." Recent Advances in Computer Science and Communications 13, no. 2 (June 3, 2020): 200–214. http://dx.doi.org/10.2174/2213275912666181127124711.

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Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

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A., Bharath, and Sriganesh Madhvanath. "Allograph modeling for online handwritten characters in devanagari using constrained stroke clustering." ACM Transactions on Asian Language Information Processing 13, no. 3 (October 3, 2014): 1–21. http://dx.doi.org/10.1145/2629622.

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SANTOSH, K. C., CHOLWICH NATTEE, and BART LAMIROY. "RELATIVE POSITIONING OF STROKE-BASED CLUSTERING: A NEW APPROACH TO ONLINE HANDWRITTEN DEVANAGARI CHARACTER RECOGNITION." International Journal of Image and Graphics 12, no. 02 (April 2012): 1250016. http://dx.doi.org/10.1142/s0219467812500167.

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In this paper, we propose a new scheme for Devanagari natural handwritten character recognition. It is primarily based on spatial similarity-based stroke clustering. A feature of a stroke consists of a string of pen-tip positions and directions at every pen-tip position along the trajectory. It uses the dynamic time warping algorithm to align handwritten strokes with stored stroke templates and determine their similarity. Experiments are carried out with the help of 25 native writers and a recognition rate of approximately 95% is achieved. Our recognizer is robust to a large range of writing style and handles variation in the number of strokes, their order, shapes and sizes and similarities among classes.

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Samanta, Roopkatha, Soulib Ghosh, Agneet Chatterjee, and Ram Sarkar. "A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays." International Journal of Computer Vision and Image Processing 10, no. 3 (July 2020): 1–17. http://dx.doi.org/10.4018/ijcvip.2020070101.

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Due to the enormous application, handwritten digit recognition (HDR) has become an extremely important domain in optical character recognition (OCR)-related research. The predominant challenges faced in this domain include different photometric inconsistencies together with computational complexity. In this paper, the authors proposed a language invariant shape-based feature descriptor using the refraction property of light rays. It is to be noted that the proposed approach is novel as an adaptation of refraction property is completely new in this domain. The proposed method is assessed using five datasets of five different languages. Among the five datasets, four are offline (written Devanagari, Bangla, Arabic, and Telugu) and one is online (written in Assamese) handwritten digit datasets. The approach provides admirable outcomes for online digits whereas; it yields satisfactory results for offline handwritten digits. The method gives good result for both online and offline handwritten digits, which proves its robustness. It is also computationally less expensive compared to other state-of-the-art methods including deep learning-based models.

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Srivastav, Ankita, and Neha Sahu. "Segmentation of Devanagari Handwritten Characters." International Journal of Computer Applications 142, no. 14 (May 18, 2016): 15–18. http://dx.doi.org/10.5120/ijca2016909994.

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Yadav, Bharati, Ajay Indian, and Gaurav Meena. "HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters." Journal of Autonomous Intelligence 6, no. 2 (August 15, 2023): 679. http://dx.doi.org/10.32629/jai.v6i2.679.

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<p>Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.</p>

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MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (August 2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.

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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17

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Padmaja, Kannuru. "Devanagari Handwritten Character Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 102–5. http://dx.doi.org/10.22214/ijraset.2022.39744.

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Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

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Kapoor, Shuchi, and Vivek Verma. "Fragmentation of Handwritten Touching Characters in Devanagari Script." International Journal of Information Technology, Modeling and Computing 2, no. 1 (February 28, 2014): 11–21. http://dx.doi.org/10.5121/ijitmc.2014.2102.

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Deore, Shalaka P. "DHCR_SmartNet: A smart Devanagari Handwritten Character Recognition using Level-wised CNN Architecture." Computer Science 23, no. 3 (October 2, 2022): 303. http://dx.doi.org/10.7494/csci.2022.23.3.4487.

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Handwritten Script Recognition is a vital application of Machine Learning domain. Applications like automatic number plate detection, pin code detection and managing historical documents increasing more attention towards handwritten script recognition. English is the most widely spoken language, hence there has been a lot of research into identifying a script using a machine. Devanagari is popular script used by a huge number of people in the Indian Subcontinent. In this paper, level-wised efficient transfer learning approach presented on VGG16 model of Convolutional Neural Network (CNN) for identification of Devanagari isolated handwritten characters. In this work a new dataset of Devanagari characters is presented and made accessible publicly. Newly created dataset comprises 5800 samples for 12 vowels, 36 consonants and 10 digits. Initially simple CNN is implemented and trained on this new small dataset. In next stage transfer learning approach is implemented on VGG16 model and in last stage fine-tuned efficient VGG16 model is implemented. The training and testing accuracy of fine-tuned model are obtained as 98.16% and 96.47% respectively.

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Prabhanjan, S., and R. Dinesh. "Deep Learning Approach for Devanagari Script Recognition." International Journal of Image and Graphics 17, no. 03 (July 2017): 1750016. http://dx.doi.org/10.1142/s0219467817500164.

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In this paper, we have proposed a new technique for recognition of handwritten Devanagari Script using deep learning architecture. In any OCR or classification system extracting discriminating feature is most important and crucial step for its success. Accuracy of such system often depends on the good feature representation. Deciding upon the appropriate features for classification system is highly subjective and requires lot of experience to decide proper set of features for a given classification system. For handwritten Devanagari characters it is very difficult to decide on optimal set of good feature to get good recognition rate. These methods use raw pixel values as features. Deep Learning architectures learn hierarchies of features. In this work, first image is preprocessed to remove noise, converted to binary image, resized to fixed size of 30[Formula: see text][Formula: see text][Formula: see text]40 and then convert to gray scale image using mask operation, it blurs the edges of the images. Then we learn features using an unsupervised stacked Restricted Boltzmann Machines (RBM) and use it with the deep belief network for recognition. Finally network weight parameters are fine tuned by supervised back propagation learning to improve the overall recognition performance. The proposed method has been tested on large set of handwritten numerical, character, vowel modifiers and compound characters and experimental results reveals that unsupervised method yields very good accuracy of (83.44%) and upon fine tuning of network parameters with supervised learning yields accuracy of (91.81%) which is the best results reported so far for handwritten Devanagari characters.

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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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More, Vijay, and Madan Kharat. "Segmentation of Lines and Words of Handwritten Devanagari Text using Connected Components with Statistics Method." Journal of Scientific Research 66, no. 02 (2022): 179–88. http://dx.doi.org/10.37398/jsr.2022.660224.

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The pre-processing activities for handwritten Devanagari text recognition includes an significant step called Segmentation. The segmentation accuracy of Devanagari text characters depends entirely on the accurately segmented lines and words in the handwritten documents. The process of segmenting lines and words correctly leads to many issues. More detailed information is lagging on the segmentation of lines and words from Devanagari text documents, whereas it is available more for other script documents in the literature. Here, we accomplished the task of segmenting the lines and words using Connected Components with Statistics Method on PHDIndic_11 dataset. Experimentation using above mentioned method resulted in line segmentation accuracy of 91.91% and word segmentation accuracy of 72.89% which outperforms over Global threshold and Otsu’s optimum threshold methods.

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Devi, N. "Offline Handwritten Character Recognition using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1483–89. http://dx.doi.org/10.22214/ijraset.2021.37610.

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Abstract: This paper focuses on the task of recognizing handwritten Hindi characters using a Convolutional Neural Network (CNN) based. The recognized characters can then be stored digitally in the computer or used for other purposes. The dataset used is obtained from the UC Irvine Machine Learning Repository which contains 92,000 images divided into training (80%) and test set (20%). It contains different forms of handwritten Devanagari characters written by different individuals which can be used to train and test handwritten text recognizers. It contains four CNN layers followed by three fully connected layers for recognition. Grayscale handwritten character images are used as input. Filters are applied on the images to extract different features at each layer. This is done by the Convolution operation. The two other main operations involved are Pooling and Flattening. The output of the CNN layers is fed to the fully connected layers. Finally, the chance or probability score of each character is determined and the character with the highest probability score is shown as the output. A recognition accuracy of 98.94% is obtained. Similar models exist for the purpose, but the proposed model achieved a better performance and accuracy than some of the earlier models. Keywords: Devanagari characters, Convolutional Neural Networks, Image Processing

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Bhat, Mohammad Idrees, and B. Sharada. "Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals." Journal of Intelligent Systems 29, no. 1 (July 21, 2018): 799–813. http://dx.doi.org/10.1515/jisys-2017-0448.

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Abstract Interpretation of different writing styles, unconstrained cursiveness and relationship between different primitive parts is an essential and challenging task for recognition of handwritten characters. As feature representation is inadequate, appropriate interpretation/description of handwritten characters seems to be a challenging task. Although existing research in handwritten characters is extensive, it still remains a challenge to get the effective representation of characters in feature space. In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the robust graph representation and spectral graph embedding concept to characterise and effectively represent handwritten characters, taking into account writing styles, cursiveness and relationships. For corroboration of the efficacy of the proposed method, extensive experiments were carried out on the standard handwritten numeral Computer Vision Pattern Recognition, Unit of Indian Statistical Institute Kolkata dataset. The experimental results demonstrate promising findings, which can be used in future studies.

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Singh, Pratibha, Ajay Verma, and Narendra S. Chaudhari. "On the Performance Improvement of Devanagari Handwritten Character Recognition." Applied Computational Intelligence and Soft Computing 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/193868.

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The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers.L2-weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals.

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Jangid, Mahesh, and Sumit Srivastava. "Handwritten Devanagari Similar Character Recognition by Fisher Linear Discriminant and Pairwise Classification." International Journal of Image and Graphics 18, no. 04 (October 2018): 1850022. http://dx.doi.org/10.1142/s0219467818500225.

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The research works in Handwritten Devanagari Characters are continually evolving into new challenges, which exposed the new sources of further research work like, character normalization, gray-level normalization, a discrimination of the similar characters and many more. This paper discusses the discrimination of the similar characters, which is one of the major sources of classification error. The similar shape character has a very minute difference, which is called critical region and used to discriminate them by human beings. The primary goal of the current work is to identify the critical region of the similar character and use the same to generate additional features in order to minimize the classification errors in the end results. It is also quite challenging to identify the critical region as the characters are written in different handwriting styles and fonts. The paper suggestes the Fisher linear discriminant model to detect the critical region, which is used to extract the additional feature. The experiments work was conducted on the standard database, which has 36[Formula: see text]172 handwritten Devanagari characters and significant improvement has been recorded by the aforesaid technique.

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R.Birajdar, Deepali, and Manasi M.Patil. "Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction." International Journal of Computer Applications 120, no. 3 (June 18, 2015): 1–4. http://dx.doi.org/10.5120/21204-3883.

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Guha, Riya, Nibaran Das, Mahantapas Kundu, Mita Nasipuri, and K. C. Santosh. "DevNet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (April 30, 2020): 2052009. http://dx.doi.org/10.1142/s0218001420520096.

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The writing style is a unique characteristic of a human being as it varies from one person to another. Due to such diversity in writing style, handwritten character recognition (HCR) under the purview of pattern recognition is not trivial. Conventional methods used handcrafted features that required a-priori domain knowledge, which is always not feasible. In such a case, extracting features automatically could potentially attract more interests. For this, in the literature, convolutional neural network (CNN) has been a popular approach to extract features from the image data. However, state-of-the-art works do not provide a generic CNN model for character recognition, Devanagari script, for instance. Therefore, in this work, we first study several different CNN models on publicly available handwritten Devanagari characters and numerals datasets. This means that our study is primarily focusing on comparative study by taking trainable parameters, training time and memory consumption into account. Later, we propose and design DevNet, a modified CNN architecture that produced promising results, since computational complexity and memory space are our primary concerns in design.

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Naik, Vishal A., and Apurva A. Desai. "Online Handwritten Gujarati Word Recognition." International Journal of Computer Vision and Image Processing 9, no. 1 (January 2019): 35–50. http://dx.doi.org/10.4018/ijcvip.2019010103.

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In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.

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Ekbote, Maitreyi, Aishwary Jadhav, and Dayanand Ambawade. "Implementing a Hybrid Deep Learning Approach to Achieve Classic Handwritten Alphanumeric MODI Recognition." International Journal of Engineering and Advanced Technology 12, no. 1 (October 30, 2022): 58–62. http://dx.doi.org/10.35940/ijeat.a3846.1012122.

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MODI, synonymous with the Devanagari script, is an ancient script from the 17th century used by the Maratha empire as a symbol of culture and power to propagate Marathi. Due to a decline in its usage, absence of quality script database and an unavailability of good literature, identification and translation of MODI script is demanding. The present work deals with a novel study on the recognition of MODI characters and numerals by using Convolutional Neural Network (CNN) architecture. By using a traditional machine learning classifier, classification is performed, and then through a comparative analysis of Random Forest and XGBoost, the study achieves recognition accuracy of 92% for characters and 93.3% for numerals.

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Narang, Sonika Rani, M. K. Jindal, Shruti Ahuja, and Munish Kumar. "On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features." Soft Computing 24, no. 22 (May 7, 2020): 17279–89. http://dx.doi.org/10.1007/s00500-020-05018-z.

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S, Prabhanjan, and R. Dinesh. "Handwritten Devanagari Characters and Numeral Recognition using Multi-Region Uniform Local Binary Pattern." International Journal of Multimedia and Ubiquitous Engineering 11, no. 3 (March 31, 2016): 387–98. http://dx.doi.org/10.14257/ijmue.2016.11.3.37.

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Susan, Seba, and Jatin Malhotra. "Recognising Devanagari Script by Deep Structure Learning of Image Quadrants." DESIDOC Journal of Library & Information Technology 40, no. 05 (November 4, 2020): 268–71. http://dx.doi.org/10.14429/djlit.40.05.16336.

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Ancient Indic languages were written in the Devanagari script from which most of the modern-day Indic writing systems have evolved. The digitisation of ancient Devanagari manuscripts, now archived in national museums, is a part of the language documentation and digital archiving initiative of the Government of India. The challenge in digitizing these handwritten scripts is the lack of adequate datasets for training machine learning models. In our work, we focus on the Devanagari script that has 46 categories of characters that makes training a difficult task, especially when the number of samples are few. We propose deep structure learning of image quadrants, based on learning the hidden state activations derived from convolutional neural networks that are trained separately on five image quadrants. The second phase of our learning module comprises of a deep neural network that learns the hidden state activations of the five convolutional neural networks, fused by concatenation. The experiments prove that the proposed deep structure learning outperforms the state of the art.

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Moharkar, Lalita, Sudhanshu Varun, Apurva Patil, and Abhishek Pal. "A scene perception system for visually impaired based on object detection and classification using CNN." ITM Web of Conferences 32 (2020): 03039. http://dx.doi.org/10.1051/itmconf/20203203039.

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In this paper we have developed a system for visually impaired people using OCR and machine learning. Optical Character Recognition is an automated data entry tool. To convert handwritten, typed or printed text into data that can be edited on a computer, OCR software is used. The paper documents are scanned on simple systems with an image scanner. Then, the OCR program looks at the image and compares letter shapes to stored letter images. OCR in English has evolved over the course of half a century to a point that we have established application that can seamlessly recognize English text. This may not be the case for Indian languages, as they are much more complex in structure and computation compared to English. Therefore, creating an OCR that can execute Indian languages as suitably as it does for English becomes a must. Devanagari is one of the Indian languages spoken by more than 70% of people in Maharashtra, so some attention should be given to studying ancient scripts and literature. The main goal is to develop a Devanagari character recognition system that can be implemented in the Devanagari script to recognize different characters, as well as some words.

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Shirkande, Prof Aparna S., Sakshi S. Sawant, Neha V. Shinde, and Sharanya S. Rao. "Study on the OCR of the Devanagari script using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1502–6. http://dx.doi.org/10.22214/ijraset.2022.46874.

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bstract: Optical Character Recognition (OCR) is the electronic conversion of images of typed, handwritten or printed text into machine encoded text, whether from scanned document, a photo of a document, a scene photo. The Optical Character Recognition is emerging as a useful technology for data entry, digital data storage and as an aid to visually impaired people. The Devanagari script is composed of 47 primary characters including 14 vowels and 33 consonants. It is fourth most widely adopted writing system in the world being used for 120 languages. Convolution Neural Network (CNN) are a specialized type of artificial neural networks that use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in

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Gupta, Deepika, and Soumen Bag. "Holistic versus segmentation-based recognition of handwritten Devanagari conjunct characters: a CNN-based experimental study." Neural Computing and Applications 34, no. 7 (January 10, 2022): 5665–81. http://dx.doi.org/10.1007/s00521-021-06672-6.

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Dargan, Shaveta, Munish Kumar, Anupam Garg, and Kutub Thakur. "Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM." Soft Computing 24, no. 13 (November 14, 2019): 10111–22. http://dx.doi.org/10.1007/s00500-019-04525-y.

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Kanmani, Dr S., B. Sujitha, K. Subalakshmi, S. Umamaheswari, and Karimreddy Punya Sai Teja Reddy. "Off-Line and Online Handwritten Character Recognition Using RNN-GRU Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2518–26. http://dx.doi.org/10.22214/ijraset.2023.50184.

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Abstract: Recognizing handwritten characters is an extremely difficult task in the domains of pattern recognition and computer vision. It involves the use of a process that enables computers to identify and convert handwritten or printed characters, such as letters and numbers, into a digital format that is usable by the computer. Currently, the RNN-CNN hybrid algorithm is employed to predict handwritten text in images with an accuracy rate of 91.5%. However, the existing system can only recognize characters and words character-by-character and word-by-word. The proposed system aims to address this limitation by enabling line-byline recognition and the conversion of handwritten text to OCR. To achieve this, the system utilizes the GRU algorithm to predict the next letter in incomplete words. Furthermore, the IAM dataset, consisting of 135,000 annotated sentences, is utilized to detect and rectify spelling errors in texts.

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Ghosh, Rajib, Chirumavila Vamshi, and Prabhat Kumar. "RNN based online handwritten word recognition in Devanagari and Bengali scripts using horizontal zoning." Pattern Recognition 92 (August 2019): 203–18. http://dx.doi.org/10.1016/j.patcog.2019.03.030.

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Ma, Ming, Dong-Won Park, Soo Kyun Kim, and Syungog An. "Online Recognition of Handwritten Korean and English Characters." Journal of Information Processing Systems 8, no. 4 (December 31, 2012): 653–68. http://dx.doi.org/10.3745/jips.2012.8.4.653.

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Mehta, Abhishek, Subhashchandra Desai, and Ashish Chaturvedi. "Handwritten Hindi Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks." International Journal of Information Systems and Informatics 1, no. 1 (October 14, 2020): 1–15. http://dx.doi.org/10.47747/ijisi.v1i1.77.

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Manually written character acknowledgment is as of now getting the consideration of scientists in view of potential applications in helping innovation for dazzle and outwardly hindered clients, human–robot collaboration, programmed information passage for business reports, and so on. In this work, we propose a strategy to perceive transcribed Devanagari characters utilizing profound convolutional neural organizations (DCNN) which are one of the ongoing procedures embraced from the profound learning network. We tested the ISIDCHAR information base gave by (Information Sharing Index) ISI, Kolkata and V2DMDCHAR information base with six distinct structures of DCNN to assess the exhibition and furthermore research the utilization of six as of late created versatile inclination strategies. A layer-wise method of DCNN has been utilized that assisted with accomplishing the most noteworthy acknowledgment exactness and furthermore get a quicker union rate. The consequences of layer-wise-prepared DCNN are great in correlation with those accomplished by a shallow strategy of high quality highlights and standard DCNN

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Zeng, Wei, XiangXu Meng, ChengLei Yang, and Lei Huang. "Feature extraction for online handwritten characters using Delaunay triangulation." Computers & Graphics 30, no. 5 (October 2006): 779–86. http://dx.doi.org/10.1016/j.cag.2006.07.007.

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Sen, Shibaprasad, Mridul Mitra, Ankan Bhattacharyya, Ram Sarkar, Friedhelm Schwenker, and Kaushik Roy. "Feature Selection for Recognition of Online Handwritten Bangla Characters." Neural Processing Letters 50, no. 3 (February 27, 2019): 2281–304. http://dx.doi.org/10.1007/s11063-019-10010-2.

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Hasan, Md Al Mehedi, Jungpil Shin, and Md Maniruzzaman. "Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine." Applied Sciences 12, no. 20 (October 12, 2022): 10249. http://dx.doi.org/10.3390/app122010249.

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Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters.

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Kaur, Rajandeep, Rajneesh Rani, and Roop Pahuja. "Text-Dependent and Text-Independent Writer Identification Approaches." International Journal of Software Innovation 10, no. 1 (January 2022): 1–23. http://dx.doi.org/10.4018/ijsi.297514.

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Writer identification is a wide-spreading biometric which can be used as a legitimate mean to identify an individual. It facilitates the experts to automatically identify the person in many security concerns applications such as forensic science. Due to this, much attention has been drawn in this field from the last few decades. On the basis of input text, it can have various forms like online, offline, text-dependent or text-independent writer identification. The paper will present a systematic study on text-dependent and text-independent writer identification of handwritten text images for various Indic and non-Indic scripts. The various segmentation techniques used to segment handwritten text are also presented in detail. The various datasets available for researchers are given for various scripts such as English, Arabic, Chinese, Japanese, Dutch, Farsi, Devanagari, Bangla, and Kannada discussed by doing exhaustive analysis of various studies. We hope that our research will be helpful in giving better understanding of the area and provides various directions for further research.

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Bisht, Mamta, and Richa Gupta. "Fine-Tuned Pre-Trained Model for Script Recognition." International Journal of Mathematical, Engineering and Management Sciences 6, no. 5 (October 1, 2021): 1297–314. http://dx.doi.org/10.33889/ijmems.2021.6.5.078.

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Script recognition is the first necessary preliminary step for text recognition. In the deep learning era, for this task two essential requirements are the availability of a large labeled dataset for training and computational resources to train models. But if we have limitations on these requirements then we need to think of alternative methods. This provides an impetus to explore the field of transfer learning, in which the previously trained model knowledge established in the benchmark dataset can be reused in another smaller dataset for another task, thus saving computational power as it requires to train only less number of parameters from the total parameters in the model. Here we study two pre-trained models and fine-tune them for script classification tasks. Firstly, the VGG-16 pre-trained model is fine-tuned for publically available CVSI-15 and MLe2e datasets for script recognition. Secondly, a well-performed model on Devanagari handwritten characters dataset has been adopted and fine-tuned for the Kaggle Devanagari numeral dataset for numeral recognition. The performance of proposed fine-tune models is related to the nature of the target dataset as similar or dissimilar from the original dataset and it has been analyzed with widely used optimizers.

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SEN, AMRIK, G. ANANTHAKRISHNAN, SURESH SUNDARAM, and A. G. RAMAKRISHNAN. "DYNAMIC SPACE WARPING OF STROKES FOR RECOGNITION OF ONLINE HANDWRITTEN CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 05 (August 2009): 925–43. http://dx.doi.org/10.1142/s0218001409007375.

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This paper suggests a scheme for classifying online handwritten characters, based on dynamic space warping of strokes within the characters. A method for segmenting components into strokes using velocity profiles is proposed. Each stroke is a simple arbitrary shape and is encoded using three attributes. Correspondence between various strokes is established using Dynamic Space Warping. A distance measure which reliably differentiates between two corresponding simple shapes (strokes) has been formulated thus obtaining a perceptual distance measure between any two characters. Tests indicate an accuracy of over 85% on two different datasets of characters.

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Li, Ling Hua, Shou Fang Mi, and Heng Bo Zhang. "Template-Based Handwritten Numeric Character Recognition." Advanced Materials Research 586 (November 2012): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.586.384.

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This paper describes a stroke-based handwriting analysis method in classifying handwritten Numeric characters by using a template-based approach. Writing strokes are variable from time to time, even when the writing character is same and comes from the same user. Writing strokes include the properties such as the number of the strokes, the shapes and sizes of them and the writing order and the writing speed. We describe here a template-based system using the properties of writing strokes for the recognition of online handwritten numeric characters. Experimental results show that within the 1500 numeric characters taken from 30 writers, the system got 97.84% recognition accuracy which is better than other systems shown by other literatures.

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Deore, Shalaka, and Albert Pravin. "Histogram of Oriented Gradients Based Off-Line Handwritten Devanagari Characters Recognition Using SVM, K-NN and NN Classifiers." Revue d'Intelligence Artificielle 33, no. 6 (December 30, 2019): 441–46. http://dx.doi.org/10.18280/ria.330606.

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BOUSLAMA, FAOUZI. "STRUCTURAL AND FUZZY TECHNIQUES IN THE RECOGNITION OF ONLINE ARABIC CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 1027–40. http://dx.doi.org/10.1142/s0218001499000574.

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This paper describes a new hybrid approach for the automatic recognition of handwritten Arabic characters. The algorithm is based on structural techniques and fuzzy logic. Local features such as lines, curves, diacritic points are extracted from the geometry and topology of characters. Fuzzy linguistic variables are used to model the features and provide a suitable mean to vaguely describe the many styles and variations in the writing system. The combination of local features provide a natural way to describe characters in a compact style. Fuzzy if–then rules are used for classification. This hybrid technique is efficient for large and complex sets such as Arabic characters.

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Abuzaraida, Mustafa Ali, Mohammed Elmehrek, and Esam Elsomadi. "Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3584. http://dx.doi.org/10.11591/ijece.v11i4.pp3584-3592.

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With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.

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Singh, Harjeet, R. K. Sharma, V. P. Singh, and Munish Kumar. "Recognition of online handwritten Gurmukhi characters using recurrent neural network classifier." Soft Computing 25, no. 8 (February 10, 2021): 6329–38. http://dx.doi.org/10.1007/s00500-021-05620-9.

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Ren, Haiqing, Weiqiang Wang, and Chenglin Liu. "Recognizing online handwritten Chinese characters using RNNs with new computing architectures." Pattern Recognition 93 (September 2019): 179–92. http://dx.doi.org/10.1016/j.patcog.2019.04.015.

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Yang, Liping, and Ramjee Prasad. "Online recognition of handwritten characters using differential angles and structural descriptors." Pattern Recognition Letters 14, no. 12 (December 1993): 1019–24. http://dx.doi.org/10.1016/0167-8655(93)90010-b.

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Zhong, Yingna, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, and Kohbalan Moorthy. "Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 4 (July 20, 2023): 567–75. http://dx.doi.org/10.20965/jaciii.2023.p0567.

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Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.

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Ghosh, Rajib, Partha Pratim Roy, and Prabhat Kumar. "Smart Device Authentication Based on Online Handwritten Script Identification and Word Recognition in Indic Scripts Using Zone-Wise Features." International Journal of Information System Modeling and Design 9, no. 1 (January 2018): 21–55. http://dx.doi.org/10.4018/ijismd.2018010102.

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Secure authentication is a vital component for device security. The most basic form of authentication is by using passwords. With the evolution of smart devices, selecting stronger and unbreakable passwords have become a challenging task. Such passwords if written in native languages tend to offer improved security since attackers having no knowledge of such scripts finding it hard to crack. This article proposes two zone-wise feature extraction approaches - zone-wise structural and directional (ZSD) and zone-wise slopes of dominant points (ZSDP), to recognize online handwritten script and word in four major Indic scripts - Devanagari, Bengali, Telugu and Tamil. These features have been used separately and in combination in HMM-based platform for recognition purpose. The dimension reduction of the ZSD-ZSDP combination with factor analysis has shown the best performance in all the four scripts. This work can be utilized for setting up the authentication schemes with the Indic scripts' passwords thus rendering it difficult to crack by hackers having no knowledge of such scripts.

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Chang, Yun, Jia Lee, Omar Rijal, and Syed Bakar. "Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model." International Journal of Applied Mathematics and Computer Science 20, no. 4 (December 1, 2010): 727–38. http://dx.doi.org/10.2478/v10006-010-0055-x.

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Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship modelThis paper presents novel feature extraction and classification methods for online handwritten Chinese character recognition (HCCR). TheX-graph andY-graph transformation is proposed for deriving a feature, which shows useful properties such as invariance to different writing styles. Central to the proposed method is the idea of capturing the geometrical and topological information from the trajectory of the handwritten character using theX-graph and theY-graph. For feature size reduction, the Haar wavelet transformation was applied on the graphs. For classification, the coefficient of determination (R2p) from the two-dimensional unreplicated linear functional relationship model is proposed as a similarity measure. The proposed methods show strong discrimination power when handling problems related to size, position and slant variation, stroke shape deformation, close resemblance of characters, and non-normalization. The proposed recognition system is applied to a database with 3000 frequently used Chinese characters, yielding a high recognition rate of 97.4% with reduced processing time of 75.31%, 73.05%, 58.27% and 40.69% when compared with recognition systems using the city block distance with deviation (CBDD), the minimum distance (MD), the compound Mahalanobis function (CMF) and the modified quadratic discriminant function (MQDF), respectively. High precision rates were also achieved.

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Singh, Mandeep, Karun Verma, Bob Gill, and Ramandeep Kaur. "Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 90. http://dx.doi.org/10.14419/ijet.v7i3.4.16753.

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Online handwriting character recognition is gaining attention from the researchers across the world because with the advent of touch based devices, a more natural way of communication is being explored. Stroke based online recognition system is proposed in this paper for a very complex Gurmukhi script. In this effort, recognition for 35 basic characters of Gurmukhi script has been implemented on the dataset of 2019 Gurmukhi samples. For this purpose, 32 stroke classes have been considered. Three types of features have been extracted. Hybrid of these features has been proposed in this paper to train the classification models. For stroke classification, three different classifiers namely, KNN, MLP and SVM are used and compared to evaluate the effectiveness of these models. A very promising “stroke recognition rate” of 94% by KNN, 95.04% by MLP and 95.04% by SVM has been obtained.

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Pei, Leisi, and Guang Ouyang. "Online recognition of handwritten characters from scalp-recorded brain activities during handwriting." Journal of Neural Engineering 18, no. 4 (May 28, 2021): 046070. http://dx.doi.org/10.1088/1741-2552/ac01a0.

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Journal articles on the topic 'Devanagari online handwritten characters'

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