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Journal articles on the topic 'Handwritten'

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Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Mohammed, Israa Bashir, Bashar Saadoon Mahdi, and Mustafa Salam Kadhm. "Handwritten signature identification based on MobileNets model and support vector machine classifier." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2401–9. http://dx.doi.org/10.11591/beei.v12i4.4965.

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Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.

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Mohammed, Israa Bashir, Bashar Saadoon Mahdi, and Mustafa Salam Kadhm. "Handwritten signature identification based on MobileNets model and support vector machine classifier." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2401–9. http://dx.doi.org/10.11591/eei.v12i4.4965.

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Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.

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3

Shinde, Jyoti, Chaitali Rajput, and Prof Mrunal Shidore Prof Milind Rane. "Handwritten Digit Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 608–11. http://dx.doi.org/10.31142/ijtsrd8384.

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ŚNIEŻKO, DARIUSZ. "Handwritten Dedication." Autobiografia 15 (2020): 99–107. http://dx.doi.org/10.18276/au.2020.2.15-08.

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5

Donaldson, Laurie. "Handwritten circuits." Materials Today 14, no. 9 (September 2011): 379. http://dx.doi.org/10.1016/s1369-7021(11)70179-1.

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Firdous, Saniya. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1409–28. http://dx.doi.org/10.22214/ijraset.2022.42114.

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Singh, Priyanshu, Pranali Pawar, and Nikhil Raj. "Handwritten Digit Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 75–82. http://dx.doi.org/10.22214/ijraset.2022.42062.

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Abstract: Digital recognition is also remarkable an important issue. As handwritten digits are not a same size, thickness, position and direction, in this case by the way, various difficulties should be considered find the handwritten digital recognition problem. I unique and a variety of creative styles for different people moreover have an influence on the model as well the presence of digits. It is a strategy to see again edit written digits. It has a wide variety applications, for example, scheduled bank checks, post offices and tax documents and so on. The purpose of this project is to use the classification algorithm to identify handwritten digits. Background results are probably the most widely used Machine Learning Algorithms such as SVM, KNN and RFC and in-depth reading calculations like CNN multilayer using Keras and Theano and Tensorflow. Using these, 98.70% accuracy was used by CNN (Keras + Theano) compared to 97.91% using SVM, 96.67% using KNN, 96.89% using RFC was obtained. Keywords: SVM, RFC, KNN, CNN

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Saiteja, Jeisetti, Podila Srivally Rao, and P. Mani Bharadwaj. "HANDWRITTEN DIGIT RECOGNITION." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 45–54. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.007.

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This project dives into the fundamentals of machine learning victimization associate degree approachable and well-known artificial language, Python. And here we'll be reviewing 2 main components: 1st, we'll be learning regarding the aim of Machine Learning and wherever it applies to the important world. Second, we'll get a general summary of Machine learning topics like supervised learning, model analysis, and Machine Learning algorithms.

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Aref, Walid, Daniel Barbará, and Padmavathi Vallabhaneni. "The handwritten trie." ACM SIGMOD Record 24, no. 2 (May 22, 1995): 151–62. http://dx.doi.org/10.1145/568271.223811.

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Thomas, Achint Oommen, Amalia Rusu, and Venu Govindaraju. "Synthetic handwritten CAPTCHAs." Pattern Recognition 42, no. 12 (December 2009): 3365–73. http://dx.doi.org/10.1016/j.patcog.2008.12.018.

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11

Ruparelia, Kaushil, Ashay Shah, Seema Wadhwani, and M. Mani. "Handwritten Manuscript Digitizer." International Journal of Computer Applications 136, no. 6 (February 17, 2016): 24–27. http://dx.doi.org/10.5120/ijca2016908467.

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Perea, Manuel, Ana Marcet, Beatriz Uixera, and Marta Vergara-Martínez. "Eye movements when reading sentences with handwritten words." Quarterly Journal of Experimental Psychology 71, no. 1 (January 2018): 20–27. http://dx.doi.org/10.1080/17470218.2016.1237531.

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The examination of how we read handwritten words (i.e., the original form of writing) has typically been disregarded in the literature on reading. Previous research using word recognition tasks has shown that lexical effects (e.g., the word-frequency effect) are magnified when reading difficult handwritten words. To examine this issue in a more ecological scenario, we registered the participants’ eye movements when reading handwritten sentences that varied in the degree of legibility (i.e., sentences composed of words in easy vs. difficult handwritten style). For comparison purposes, we included a condition with printed sentences. Results showed a larger reading cost for sentences with difficult handwritten words than for sentences with easy handwritten words, which in turn showed a reading cost relative to the sentences with printed words. Critically, the effect of word frequency was greater for difficult handwritten words than for easy handwritten words or printed words in the total times on a target word, but not on first-fixation durations or gaze durations. We examine the implications of these findings for models of eye movement control in reading.

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Sharma, Sandhya, and Sheifali Gupta. "Analyzing Different Optimizers for the Recognition of Gurmukhi Handwritten Text by Employing CNN." ECS Transactions 107, no. 1 (April 24, 2022): 8809–17. http://dx.doi.org/10.1149/10701.8809ecst.

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Automatic recognition of handwritten data is an important application area in various fields. Recognition of handwritten text, which is cursive in nature, is a cumbersome task. Moreover, handwritten text by different writers makes the recognition even more difficult due to the different writing styles of the individuals. In this paper, a CNN model employing different optimizers is proposed for the recognition of Gurmukhi handwritten dataset. For the purpose of classification and recognition, five different classes of Gurmukhi handwritten text have been created where each class has 1,000 handwritten samples. Results are obtained using four different optimizers: Adagrad, Adam, Adamax, and RMS prop. Maximum validation accuracy of 99.20% is achieved using Adam optimizer.

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Jehangir, Sardar, Sohail Khan, Sulaiman Khan, Shah Nazir, and Anwar Hussain. "Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis." January 2021 40, no. 1 (January 1, 2021): 152–59. http://dx.doi.org/10.22581/muet1982.2101.14.

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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.

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Alharbi, Abir. "A Genetic-LVQ neural networks approach for handwritten Arabic character recognition." Artificial Intelligence Research 7, no. 2 (November 26, 2018): 43. http://dx.doi.org/10.5430/air.v7n2p43.

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Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

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Wijaya, Aditya Surya, Nurul Chamidah, and Mayanda Mega Santoni. "Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 1 (April 30, 2019): 33. http://dx.doi.org/10.22146/ijeis.38729.

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Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features reduced by K-Support Vector Nearest Neighbor (K-SVNN) and for recognizing handwritten pattern from testing data, we used K-Nearest Neighbor (KNN). Testing result shows that reducing training data using K-SVNN able to improve handwritten character recognition accuracy.

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17

Shin, Jungpil, Md Maniruzzaman, Yuta Uchida, Md Al Mehedi Hasan, Akiko Megumi, Akiko Suzuki, and Akira Yasumura. "Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods." Applied Sciences 12, no. 10 (May 23, 2022): 5256. http://dx.doi.org/10.3390/app12105256.

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The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data.

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Ali Nur, Mukerem, Mesfin Abebe, and Rajesh Sharma Rajendran. "Handwritten Geez Digit Recognition Using Deep Learning." Applied Computational Intelligence and Soft Computing 2022 (November 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/8515810.

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Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Geez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Geez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this work, the proposed model is to recognize Geez digits using CNN. Deep neural networks, which have recently shown exceptional performance in numerous pattern recognition and machine learning applications, are used to recognize handwritten Geez digits, but this has not been attempted for Ethiopic scripts. Our dataset, which contains 51,952 images of handwritten Geez digits collected from 524 individuals, is used to train and evaluate the CNN model. The application of the CNN improves the performance of several machine-learning classification methods significantly. Our proposed CNN model has an accuracy of 96.21% and a loss of 0.2013. In comparison to earlier research works on Geez handwritten digit recognition, the study was able to attain higher recognition accuracy using the developed CNN model.

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BATUWITA, RUKSHAN, VASILE PALADE, and DHARMAPRIYA C. BANDARA. "A CUSTOMIZABLE FUZZY SYSTEM FOR OFFLINE HANDWRITTEN CHARACTER RECOGNITION." International Journal on Artificial Intelligence Tools 20, no. 03 (June 2011): 425–55. http://dx.doi.org/10.1142/s021821301100022x.

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Automated offline handwritten character recognition involves the development of computational methods that can generate descriptions of the handwritten objects from scanned digital images. This is a challenging computational task, due to the vast impreciseness associated with the handwritten patterns of different individuals. Therefore, to be successful, any solution should employ techniques that can effectively handle this imprecise knowledge. Fuzzy Logic, with its ability to deal with the impreciseness arisen due to lack of knowledge, could be successfully used to develop automated systems for handwritten character recognition. This paper presents an approach towards the development of a customizable fuzzy system for offline handwritten character recognition.

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Somashekar, Thatikonda. "A Survey on Handwritten Character Recognition using Machine Learning Technique." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 18, 2021): 1019–24. http://dx.doi.org/10.51201/jusst/21/05304.

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Due to its broad range of applications, handwritten character recognition is widespread. Processing application forms, digitizing ancient articles, processing postal addresses, processing bank checks, and many other handwritten character processing fields are increasing in popularity. Since the last three decades, handwritten characters have drawn the attention of researchers. For successful recognition, several methods have been suggested. This paper presents a comprehensive overview of handwritten character recognition using a neural network as a machine learning tool.

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Sowan, Azizeh K., Vinay U. Vaidya, Karen L. Soeken, and Elora Hilmas. "Computerized Orders with Standardized Concentrations Decrease Dispensing Errors of Continuous Infusion Medications for Pediatrics." Journal of Pediatric Pharmacology and Therapeutics 15, no. 3 (January 1, 2010): 189–202. http://dx.doi.org/10.5863/1551-6776-15.3.189.

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Abstract OBJECTIVES The use of continuous infusion medications with individualized concentrations may increase the risk for errors in pediatric patients. The objective of this study was to evaluate the effect of computerized prescriber order entry (CPOE) for continuous infusions with standardized concentrations on frequency of pharmacy processing errors. In addition, time to process handwritten versus computerized infusion orders was evaluated and user satisfaction with CPOE as compared to handwritten orders was measured. METHODS Using a crossover design, 10 pharmacists in the pediatric satellite within a university teaching hospital were given test scenarios of handwritten and CPOE order sheets and asked to process infusion orders using the pharmacy system in order to generate infusion labels. Participants were given three groups of orders: five correct handwritten orders, four handwritten orders written with deliberate errors, and five correct CPOE orders. Label errors were analyzed and time to complete the task was recorded. RESULTS Using CPOE orders, participants required less processing time per infusion order (2 min, 5 sec ± 58 sec) compared with time per infusion order in the first handwritten order sheet group (3 min, 7 sec ± 1 min, 20 sec) and the second handwritten order sheet group (3 min, 26 sec ± 1 min, 8 sec), (p<0.01). CPOE eliminated all error types except wrong concentration. With CPOE, 4% of infusions processed contained errors, compared with 26% of the first group of handwritten orders and 45% of the second group of handwritten orders (p<0.03). Pharmacists were more satisfied with CPOE orders when compared with the handwritten method (p=0.0001). CONCLUSIONS CPOE orders saved pharmacists' time and greatly improved the safety of processing continuous infusions, although not all errors were eliminated. pharmacists were overwhelmingly satisfied with the CPOE orders

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Shen, Lu, Bidong Chen, Jianjing Wei, Hui Xu, Su-Kit Tang, and Silvia Mirri. "The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review." Applied Sciences 13, no. 6 (March 9, 2023): 3500. http://dx.doi.org/10.3390/app13063500.

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Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.

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Rehman, Muhammad Zubair, Nazri Mohd. Nawi, Mohammad Arshad, and Abdullah Khan. "Recognition of Cursive Pashto Optical Digits and Characters with Trio Deep Learning Neural Network Models." Electronics 10, no. 20 (October 15, 2021): 2508. http://dx.doi.org/10.3390/electronics10202508.

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Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet; 98.82, 99.15, and 69.82 percent and CNN; 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.

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Zhu, Cheng Hui, Wen Jun Xu, Jian Ping Wang, and Xiao Bing Xu. "Research on a Characteristic Extraction Algorithm Based on Analog Space-Time Process for Off-Line Handwritten Chinese Characters." Advanced Materials Research 433-440 (January 2012): 3649–55. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3649.

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On the absence of space-time information, it is difficult to extract the character stroke feature from the off-line handwritten Chinese character image. A feature extraction algorithm is proposed based on analog space-time process by the process neural network. The handwritten Chinese character image is transformed into geometric shape by different types, different numbers, different locations, different orders and different structures of Chinese character strokes. By extracting fault-tolerant features of the five kinds of the off-line handwritten Chinese characters, the data-knowledge table of features is constructed. The parameters of process neural networks are optimized by Particle Swarm optimization (PSO). The handwritten Chinese characters are used to carry out simulation experiment in SCUT-IRAC-HCCLIB. The experiment results show that the algorithm exhibits a strong ability of cognizing handwritten Chinese characters.

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A, Mahesh, Kanagaraj D, Thirugnanam G, Vinoth Kumar, Ukesh Kumar, and Velmurugan A. "Handwritten Digit Recognition using Machine Learning with Python." International Research Journal of Computer Science 10, no. 05 (June 23, 2023): 168–71. http://dx.doi.org/10.26562/irjcs.2023.v1005.11.

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Handwritten Digit Recognition is one of the essentially significant issues in pattern recognition applications. The main purpose of this project is to build an automatic handwritten digit recognition method for the recognition of handwritten digit strings. This paper proposes a simple convolution neural network approach to handwritten digit recognition. Convolutional Neural Network model is implemented using MNIST dataset. This dataset consists 60,000 small square 28×28pixel grayscale images of handwritten single digits between 0 and 9. The applications of digit recognition include postal mail sorting, check processing, form data entry, etc. The core of the issue exists in the capacity to foster a proficient calculation that can perceive manually written digits and which is put together by clients by the method of a scanner, tablet, and other computerized gadgets.

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Amulya, K., Lakshmi Reddy, M. Chandara Kumar, and Rachana D. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering 12, no. 1 (December 30, 2022): 6–11. http://dx.doi.org/10.35940/ijitee.a9350.1212122.

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Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwritten documents during the past ten years in order to digitize them. This review paper's goals are to present research directions and a summary of previous studies on character recognition in handwritten texts. Since different people have different handwriting styles, handwritten characters might be challenging to read. Our "Digitization of handwritten notes" research and effort is to categorize and identify characters in the south Indian language of Kannada. The characters are extracted from printed texts and pre-processed using NumPy and OpenCV before being fed through a CNN

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Wang, Ruwei. "Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch." Applied and Computational Engineering 8, no. 1 (August 1, 2023): 450–55. http://dx.doi.org/10.54254/2755-2721/8/20230216.

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Thanks to advancements in machine learning and artificial intelligence techniques, computers can now practice on data and learn from it in a manner that is similar to how the human brain works. Handwritten character and number identification has been one of the most pressing and fascinating subjects in pattern recognition and image processing. One of the most urgent and intriguing topics in pattern recognition and picture processing has been the identification of handwritten characters and numbers. As a crucial part of artificial intelligence, handwritten digit identification technology provides a vast array of application possibilities. The data demonstrates that, even though handwritten numbers are simply created with a few straightforward strokes, the appearance of numbers is more variable due to the various writing styles of each person. In this study, a deep learning framework-based upgraded LeNet-5 convolutional neural network model is used to build a handwritten number recognition model in Python. Automatic recognition of handwritten numbers will become the standard recognition technique if it can be applied to a wide range of industries, including banking and accounting, and hence save human costs.

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Singh, Pawan Kumar, Ram Sarkar, Ajith Abraham, and Mita Nasipuri. "A Case Study on Handwritten Indic Script Classification: Benchmarking of the Results at Page, Block, Text-line, and Word Levels." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (March 31, 2022): 1–36. http://dx.doi.org/10.1145/3476102.

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Handwritten script classification is still considered as a challenging research problem in the domain of document image analysis. Although some research attempts have been made by the researchers for solving the challenging issues, a comprehensive solution is yet to be achieved. The case study, undertaken here, analyzes the performances of various state-of-the art handwritten script classification methods for Indian scripts where features, needed for the script classification task, are extracted from the script images at four different granularity levels, i.e., page, block, text line, or word. The results of handwritten script classification at each level have been obtained and compared using eight different feature sets and six different state-of-the-art classifiers. Based on the classification results, an ideal level for performing the handwritten script classification task is suggested among these four classification levels. The results have also been improved by using two feature dimensionality reduction methods. All these experiments are done on two different handwritten Indic script databases, of which one is an in-house developed dataset and the other one is a freely available dataset. Finally, some future research directions that may be undertaken by the researchers as an application of the handwritten Indic script classification problem are also highlighted. The work presented here provides a basic foundation for the construction of a comprehensive handwritten script classification method for official Indian scripts.

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Alsuhibany, Suliman A., Fatimah N. Almohaimeed, and Naseem A. Alrobah. "Synthetic Arabic handwritten CAPTCHA." International Journal of Information and Computer Security 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijics.2021.10034739.

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Whyte, Mandi. "Computerised versus handwritten records." Paediatric Nursing 17, no. 7 (September 2005): 15–18. http://dx.doi.org/10.7748/paed.17.7.15.s17.

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Whyte, Mandi. "Computerised versus Handwritten records." Paediatric Care 17, no. 7 (September 2005): 15–18. http://dx.doi.org/10.7748/paed2005.09.17.7.15.c997.

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Taqa, Alaa, and Hanaa Mahmood. "Arabic Handwritten Signature Identification." AL-Rafidain Journal of Computer Sciences and Mathematics 10, no. 3 (September 1, 2013): 37–54. http://dx.doi.org/10.33899/csmj.2013.163525.

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Sultan, Iklaas. "India Handwritten Digits Recognition." JOURNAL OF EDUCATION AND SCIENCE 22, no. 1 (March 1, 2009): 104–12. http://dx.doi.org/10.33899/edusj.2009.57436.

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Shinde, Rajwardhan, Onkar Dherange, Rahul Gavhane, Hemant Koul, and Nilam Patil. "HANDWRITTEN MATHEMATICAL EQUATION SOLVER." International Journal of Engineering Applied Sciences and Technology 6, no. 10 (February 1, 2022): 146–49. http://dx.doi.org/10.33564/ijeast.2022.v06i10.018.

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With recent developments in Artificial intelligence and deep learning every major field which is using computers for any type of work is trying to ease the work using deep learning methods. Deep learning is used in a wide range of fields due to its diverse range of applications like health, sports, robotics, education, etc. In deep learning, a Convolutional neural network (CNN) is being used in image classification, pattern recognition, Text classification, face recognition, live monitoring systems, handwriting recognition, Digit recognition, etc. In this paper, we propose a system for educational use where the recognition and solving process of mathematical equations will be done by machine. In this system for recognition of equations, we use a Convolutional neural network (CNN) model. The proposed system can recognize and solve mathematical equations with basic operations (-,+,/,*) of multiple digits as well as polynomial equations. The model is trained with Modified National Institute of Standards and Technology (MNIST) dataset as well as a manually prepared dataset of operator symbols (“-”,”+”, “/”, “*”, “(“, “)” ). Further, the system uses the RNN model to solve the recognized operations.

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Abhale, Poonam Bhanudas. "Handwritten English Alphabet Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 2134–39. http://dx.doi.org/10.22214/ijraset.2021.39703.

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Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the being system for handwritten character recognition. We give a literature review on colorful ways used in offline English character recognition. Keywords: Character; Character recognition; Preprocessing; Segmentation; Point birth; Bracket; neural network; Convolution neural network.

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Alsuhibany, Suliman A., Fatimah N. Almohaimeed, and Naseem A. Alrobah. "Synthetic Arabic handwritten CAPTCHA." International Journal of Information and Computer Security 16, no. 3/4 (2021): 385. http://dx.doi.org/10.1504/ijics.2021.118959.

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., Pallavi Ratra, and Harsh Khanna . "RECOGNIZING HANDWRITTEN MATHEMATICAL EXPRESSIONS." International Journal of Engineering Applied Sciences and Technology 4, no. 3 (July 31, 2019): 201–6. http://dx.doi.org/10.33564/ijeast.2019.v04i03.035.

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Ali, Ashif, and Shaista Khan. "Multiscript Handwritten Numeral Recognition." Global Sci-Tech 12, no. 1 (2020): 49. http://dx.doi.org/10.5958/2455-7110.2020.00008.7.

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Miazek, Jan. "Handwritten pre-Tridentine Pontificals." Warszawskie Studia Teologiczne 31, no. 4 (December 2, 2018): 132–45. http://dx.doi.org/10.30439/wst.2018.4.8.

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The article presents the history of pontificals, which are a bishop's liturgical books, beginning with their creation in the 9th century till the 16th century. The following pontificals are analysed in detail: Roman-Germanic Pontifical of the 10th century, Roman Pontifical of the 12th century, Roman Curia Pontifical of the 13th century and William Durand's Pontifical of the 13th century. In the article the process of geographical spreading of pontificals was also demonstrated. The history of pontificals shows how liturgical traditions were spreading and mixing with each other: Roman tradition came into contact with the tradition from the Frankish countries, and from the Frankish countries it was transferred to Rhenish countries. There the pontifical was modified and came back to Rome. In this form, thanks to the invention of printing, it spread in the whole Church.

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Epishkina, A. V., A. V. Beresneva, S. S. Babkin, A. S. Kurnev, and V. Yu Lermontov. "About handwritten signature verification." Prikladnaya diskretnaya matematika. Prilozhenie, no. 10 (September 1, 2017): 73–76. http://dx.doi.org/10.17223/2226308x/10/31.

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Namboodiri, A. M., and A. K. Jain. "Online handwritten script recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 1 (January 2004): 124–30. http://dx.doi.org/10.1109/tpami.2004.1261096.

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Alsuhibany, Suliman A., Fatimah N. Almohaimeed, and Naseem A. Alrobah. "Synthetic Arabic handwritten CAPTCHA." International Journal of Information and Computer Security 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijics.2021.10034739.

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Chajri, Yassine, and Belaid Bouikhalene. "Handwritten Mathematical Expressions Recognition." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 5 (May 31, 2013): 69–76. http://dx.doi.org/10.14257/ijsip.2016.9.5.07.

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McGlade, Kieran, Catherine Cargo, Damian Fogarty, Mairead Boohan, and Mary McMullin. "Handwritten undergraduate case reports." Clinical Teacher 9, no. 2 (March 8, 2012): 112–18. http://dx.doi.org/10.1111/j.1743-498x.2011.00494.x.

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Norris, Joseph M., Matthew D. Smith, and David R. McGowan. "Handwritten undergraduate surgical logbooks." Clinical Teacher 9, no. 4 (July 12, 2012): 272. http://dx.doi.org/10.1111/j.1743-498x.2012.00611.x.

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Chajri, Yassine, and Belaid Bouikhalene. "Handwritten mathematical symbols dataset." Data in Brief 7 (June 2016): 432–36. http://dx.doi.org/10.1016/j.dib.2016.02.060.

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Abdleazeem, Sherif, and Ezzat El-Sherif. "Arabic handwritten digit recognition." International Journal of Document Analysis and Recognition (IJDAR) 11, no. 3 (November 20, 2008): 127–41. http://dx.doi.org/10.1007/s10032-008-0073-5.

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Cao, Huaigu, Venu Govindaraju, and Anurag Bhardwaj. "Unconstrained handwritten document retrieval." International Journal on Document Analysis and Recognition (IJDAR) 14, no. 2 (November 16, 2010): 145–57. http://dx.doi.org/10.1007/s10032-010-0139-z.

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Taxt, Torfinn, Jórunn B. Ólafsdóttir, and Morten Dæhlen. "Recognition of handwritten symbols." Pattern Recognition 23, no. 11 (January 1990): 1155–66. http://dx.doi.org/10.1016/0031-3203(90)90113-y.

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Thakare, Bhushan, and Parikshit Mahalle. "Handwritten Signatures: An Understanding." International Journal of Computer Applications 139, no. 4 (April 15, 2016): 21–26. http://dx.doi.org/10.5120/ijca2016909143.

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