Journal articles: 'Handwritten numeral recognition'

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Chen, Miao Chao, and Fang Wang. "Free Handwritten Numeral Recognition Based on BP Neural Network." Advanced Materials Research 850-851 (December 2013): 909–12. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.909.

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Handwritten numeral recognition is an important branch in the field of pattern recognition, has broad application prospects. This article presents a method of using BP Neural Network to implement programme for recognition of free handwritten numerals. Scanned handwritten numeral image after preprocessing and feature extraction, classificated and recognized by the BP Neural Network. Through Matlab simulation experiments it shows that the recognition method is effective and has high recognition rate.

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Chen, Yue Fen, Jun Huan Lin, and Guo Ping Li. "Design of Online Handwritten Numeral Recognition System." Applied Mechanics and Materials 201-202 (October 2012): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.329.

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An effective online handwritten numeral recognition system is designed based on the Matlab GUI interface. The coordinate locations of the handwritten numerals are recorded, from which the stroke direction variations and the 2-dimensional distance between the starting point and ending point of the numeral are obtained as the features, which are encoded into 42 bits binary sequence, and then input to the Hopfield neural network. The associative memory function of the Hopfield neural network can implement the learning and recognition of the handwritten numeral. Testing results show that the designed system has high recognition rate and fast recognition speed.

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Akhand, M. A. H., Md Rahat-Uz-Zaman, Shadmaan Hye, and Md Abdus Samad Kamal. "Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network." Electronics 12, no. 2 (January 16, 2023): 472. http://dx.doi.org/10.3390/electronics12020472.

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Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start-end points of the numeral image, suggesting the numeral category for which measured start-end points are found close to reference start-end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods.

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Alqudah, Amin, Ali Mohammad Alqudah, Hiam Alquran, Hussein R. Al-Zoubi, Mohammed Al-Qodah, and Mahmood A. Al-Khassaweneh. "Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks." Applied Sciences 11, no. 4 (February 9, 2021): 1573. http://dx.doi.org/10.3390/app11041573.

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Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

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Ghosh, Arka P. "A Simple Statistical Method for Recognition of Hand-Written Numerals." Calcutta Statistical Association Bulletin 54, no. 1-2 (March 2003): 81–92. http://dx.doi.org/10.1177/0008068320030107.

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We explore a simple statistical method for classifying handwritten numerals. We work with numerals only in segmented form. We defined a pseudodistance function between handwritten numerals and classify each numeral based on its distance from the those in the reference set. This method, and its obvious variants, are studied on the basis of their performance on a data-set of moderate size (95 observations). The results suggest that this method may be considered in recognition of handwritten numeric characters.

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SADRI, JAVAD, CHING Y. SUEN, and TIEN D. BUI. "STATISTICAL CHARACTERISTICS OF SLANT ANGLES IN HANDWRITTEN NUMERAL STRINGS AND EFFECTS OF SLANT CORRECTION ON SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 01 (February 2010): 97–116. http://dx.doi.org/10.1142/s0218001410007816.

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A novel and efficient method for correction of slant angles in handwritten numeral strings is proposed. For the first time, the statistical distribution of slant angles in handwritten numerals is investigated and the effects of slant correction on the segmentation of handwritten numeral strings are shown. In our proposed slant correction method, utilizing geometric features, a Component Slant Angle (CSA) is estimated for each connected component independently. A weighted average is then used to compute the String Slant Angle (SSA), which is applied uniformly to correct the slant of all the components in numeral strings. Our experimental results have revealed novel statistics for slant angles of handwritten numeral strings, and also showed that slant correction can significantly improve extraction of segmentation features and segmentation accuracy of touching numerals. Comparison between our slant correction algorithm and similar algorithms in the literature show that our algorithm is more efficient, and on average it has a faster running time.

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Mahto, Manoj Kumar, Karamjit Bhatia, and Rajendra Kumar Sharma. "Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 2 (January 18, 2022): 69–82. http://dx.doi.org/10.5565/rev/elcvia.1282.

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Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals.

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Sethy, Abhisek, Prashanta Kumar Patra, and Soumya Ranjan Nayak. "A Hybrid System for Handwritten Character Recognition with High Robustness." Traitement du Signal 39, no. 2 (April 30, 2022): 567–76. http://dx.doi.org/10.18280/ts.390218.

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In the past few decades, the offline recognition of handwritten Indic scripts has received much attention of researchers. Although an intensive research has been reported for various Indic languages, limited research work is carried out for handwritten Odia, Bangla character recognition owing to their complex shapes and the unavailability of the standard datasets. This paper proposes an automated model for recognizing both handwritten of Odia characters and numerals, along with Bangla numerals with maximum optimization efficiency. The proposed model primarily deals with feature optimization parameters which mainly comprises of three parts. Firstly, the fast discrete curvelet transform (FDCT) is used to derive multidirectional features from the character images. Secondly, PCA along with LDA is used to reduce the dimension of the feature vector. The features are finally subjected to both least-squares support vector machine (LS-SVM), and random forest (RF) for classification. The effectiveness of proposed model is evaluated over three benchmark datasets such as Odia handwritten character, Bangla numeral and Odia handwritten numeral. The efficacy of proposed model achieves superiority as compared to state-of-art techniques. The discriminatory prospective of FDCT along with PCA and LDA features is establish more suitable than its counterparts.

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

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

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CHOI, SOON-MAN, and Il-SEOK OH. "A SEGMENTATION-FREE RECOGNITION OF HANDWRITTEN TOUCHING NUMERAL PAIRS USING MODULAR NEURAL NETWORK." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 06 (September 2001): 949–66. http://dx.doi.org/10.1142/s0218001401001271.

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The conventional approach to the recognition of handwritten touching numeral pairs uses a process with two steps; splitting the touching numerals and recognizing individual numerals. It shows a limitation mainly due to a large variation in touching styles between two numerals. In this paper, we adopt the segmentation-free approach, which regards a touching numeral pair as an atomic pattern. Two important issues are raised, i.e. solving the large-set classification and constructing a large-size training set. For the 100-class classification, we use a modular neural network which consists of 100 separate subnetworks. We construct the training set with a balance among 100 classes and using a sufficient amount by extracting actual samples from a numeral database and synthesizing samples with a scheme of forcing two numerals to touch. The experimental results show a promising performance.

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Abdulhussain, Sadiq H., Basheera M. Mahmmod, Marwah Abdulrazzaq Naser, Muntadher Qasim Alsabah, Roslizah Ali, and S. A. R. Al-Haddad. "A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments." Sensors 21, no. 6 (March 12, 2021): 1999. http://dx.doi.org/10.3390/s21061999.

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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.

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Liu, Jianghai, and Jie Hong. "Design of Handwritten Numeral Recognition System Based on BP Neural Network." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012016. http://dx.doi.org/10.1088/1742-6596/2025/1/012016.

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Abstract In the case of pattern recognition, handwritten numeral recognition is an important research topic in pattern recognition, and it has a very wide application in today’s information society. However, the research on numeral recognition is still in the development stage, and the recognition effect is not ideal. A handwritten numeral recognition method based on BP neural network is proposed. Firstly, the image is grayed, binarized, smoothed, denoised and normalized to extract the pixel value; Then the designed BP neural network is trained, compared with the expected results and expected structure, and the BP neural network is adjusted and modified; Finally, the trained neural network is obtained. Experiments show that the accuracy of this method for handwritten digit recognition is 85.88%.

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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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Hallur, Vishweshwarayya C., Rajendra S. Hegadi, and Ravindra S. Hegadi. "Handwritten Kannada Numerals Recognition by Using Zone Features and CNN Classifier." International Journal of Technology and Human Interaction 15, no. 4 (October 2019): 63–79. http://dx.doi.org/10.4018/ijthi.2019100106.

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The proposed system presents a pre-processing, segmentation, features extraction approach and Deep Convolutional Neural Network (DCNN) classifier for recognition of handwritten Kannada numerals. Pre-processing have different steps like median filter, gray scale to binary, normalization, thinning, skew correction and slant removal. Segmentation process contains different methods like vertical projection profile for word and novel character segmentation. Collections of best discriminable features are very important part in achieving high rate of identification in automatic numeral detection systems. Kannada is the major south Indian character verbal by about 50 million people. This article presents a well-organized and novel technique for recognition of handwritten Kannada numerals using zone and distance matrix. An appropriate feature extractor and a superior classifier play most important task in achieving high detection rate for a recognition scheme. This article determines a variety of feature extraction approaches and classification techniques which are designed to recognize handwritten numerals of Kannada script. The DCNN classifier approach is used to classify the testing samples of each Kannada handwritten numerals. The experimental result gives the acceptable performance rate.

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Upadhye, Gopal Dadarao, Uday V. Kulkarni, and Deepak T. Mane. "Improved Model Configuration Strategies for Kannada Handwritten Numeral Recognition." Image Analysis & Stereology 40, no. 3 (December 15, 2021): 181–91. http://dx.doi.org/10.5566/ias.2586.

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Handwritten numeral recognition has been an important area in the domain of pattern classification. The task becomes even more daunting when working with non-Roman numerals. While convolutional neural networks are the preferred choice for modeling the image data, the conception of techniques to obtain faster convergence and accurate results still poses an enigma to the researchers. In this paper, we present new methods for the initialization and the optimization of the traditional convolutional neural network architecture to obtain better results for Kannada numeral images. Specifically, we propose two different methods- an encoderdecoder setup for unsupervised training and weight initialization, and a particle swarm optimization strategy for choosing the ideal architecture configuration of the CNN. Unsupervised initial training of the architecture helps for a faster convergence owing to more task-suited weights as compared to random initialization while the optimization strategy is helpful to reduce the time required for the manual iterative approach of architecture selection. The proposed setup is trained on varying handwritten Kannada numerals. The proposed approaches are evaluated on two different datasets: a standard Dig-MNIST dataset and a custom-built dataset. Significant improvements across multiple performance metrics are observed in our proposed system over the traditional CNN training setup. The improvement in results makes a strong case for relying on such methods for faster and more accurate training and inference of digit classification, especially when working in the absence of transfer learning.

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Gao, Shan. "The Recognition of Handwritten Digital Based on BP Neural Network." Applied Mechanics and Materials 416-417 (September 2013): 1239–43. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1239.

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The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

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El Kessab, B., C. Daoui, B. Bouikhalene, and R. Salouan. "Isolated Handwritten Eastern Arabic Numerals Recognition Using Support Vectors Machines." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 346. http://dx.doi.org/10.11591/tijee.v15i2.1548.

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In this paper, we present a comparison between the different variations of virtual retina (grid size) in features extraction with the support vectors machines classifier for isolated handwritten Eastern Arabic numerals recognition. For this purpose we have used for pre-processing each numeral image the median filter, the thresholding, normalization and the centering techniques. Furthermore, the experements results that we have obtained demonstrate really that the most powerful method is that virtual retina size equal 20x20. This work has achieved approximately 85% of success rate for Eastern Arabic numerals database identification.

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Sethy, Abhisek, Prashanta Kumar Patra, Soumya Ranjan Nayak, and Deepak Ranjan Nayak. "A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 253. http://dx.doi.org/10.14419/ijet.v7i2.32.15578.

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Optical Character Recognition is one of the most interesting and highly motivated areas of research, which has been very much ap-preciated in different aspect to the area of digitations world. Here in this paper we have suggested a probabilistic approach for develop-ing recognition system for handwritten Odia numerals. To report a good level of recognition of Odia scripts is quite challenging with respect to other Indian scripts .All the procedure are sequentially enclosed to develop an recognition model and report a successful recognition accuracy. Here we have performed the analysis over to standard handwritten numeral database named as IITBBS Odia Numeral Database, which is collected from IIT Bhubaneswar. In the suggestive recognition system we have adopted a 2D-Gabor wavelet transformation approach for selection of feature vector. Apart from it we have also noted down the dimensional reduction to the obtained feature vector by sustaining to PCA. In order to predict high recognition rate we have followed up by RBF Neural Network classifier. In addition to it we have also evaluate different version of RBF like Gaussian and Polynomial. Performing over 400 samples each of 10 categories (400*10) number of Odia numeral images, we have maintained a well-defined training and testing ratio in the clas-sifier and achieved 98.02%, 96.8%.recognition rate for the reported classifiers.

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Guan, Bao Lin, and Li Deng Ba. "A New Way for Handwritten Numeral Recognition." Advanced Materials Research 798-799 (September 2013): 643–46. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.643.

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Handwritten numeral recognition method generally uses neural networks, the more prominent of these is BP neural network, but BP algorithm is easily get in a local minimum of the error-prone and causes slow oscillation and training , general solution for it is to optimize the structure of the algorithms first. Therefore, on the basis of the analysis of GA-BP algorithm, propose a method of making the appropriate operators of GA such as crossover and mutation probability, optimizing the weights and thresholds of BP Neural Network with the improved GA. At handwritten numeral recognition experiment, the results show that the method has faster convergence and more reliable stability, greatly improved BP neural network for learning and recognition rate.

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Ahmed, Syed Sohail, Zahid Mehmood, Imran Ahmad Awan, and Rehan Mehmood Yousaf. "A Novel Technique for Handwritten Digit Recognition Using Deep Learning." Journal of Sensors 2023 (January 30, 2023): 1–15. http://dx.doi.org/10.1155/2023/2753941.

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Handwritten digit recognition (HDR) shows a significant application in the area of information processing. However, correct recognition of such characters from images is a complicated task due to immense variations in the writing style of people. Moreover, the occurrence of several image artifacts like the existence of intensity variations, blurring, and noise complicates this process. In the proposed method, we have tried to overcome the aforementioned limitations by introducing a deep learning- (DL-) based technique, namely, EfficientDet-D4, for numeral categorization. Initially, the input images are annotated to exactly show the region of interest (ROI). In the next phase, these images are used to train the EfficientNet-B4-based EfficientDet-D4 model to detect and categorize the numerals into their respective classes from zero to nine. We have tested the proposed model over the MNIST dataset to demonstrate its efficacy and attained an average accuracy value of 99.83%. Furthermore, we have accomplished the cross-dataset evaluation on the USPS database and achieved an accuracy value of 99.10%. Both the visual and reported experimental results show that our method can accurately classify the HDR from images even with the varying writing style and under the presence of various sample artifacts like noise, blurring, chrominance, position, and size variations of numerals. Moreover, the introduced approach is capable of generalizing well to unseen cases which confirms that the EfficientDet-D4 model is an effective solution to numeral recognition.

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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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Khatri, Suman, and Irphan Ali. "Hindi Numeral Recognition using Neural Network." International Journal of Advance Research and Innovation 1, no. 3 (2013): 29–39. http://dx.doi.org/10.51976/ijari.131304.

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Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. The constant development of computer tools lead to the requirement of easier interface between the man and the computer. Handwritten character recognition may for instance be applied to Zip-Code recognition, automatic printed form acquisition, or cheques reading. The importance to these applications has led to intense research for several years in the field of off-line handwritten character recognition. „Hindi‟ the national language of India (written in Devanagri script) is world‟s third most popular language after Chinese and English. Hindi handwritten character recognition has got lot of application in different fields like postal address reading, cheques reading electronically. Recognition of handwritten Hindi characters by computer machine is complicated task as compared to typed characters, which can be easily recognized by the computer. This paper presents a scheme to recognize Hindi number numeral with the help of neural network.

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Ghosh, Tapotosh, Md Min-Ha-Zul Abedin, Shayer Mahmud Chowdhury, Zarin Tasnim, Tajbia Karim, S. M. Salim Reza, Sabrina Saika, and Mohammad Abu Yousuf. "Bangla handwritten character recognition using MobileNet V1 architecture." Bulletin of Electrical Engineering and Informatics 9, no. 6 (December 1, 2020): 2547–54. http://dx.doi.org/10.11591/eei.v9i6.2234.

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Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes.

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Näsänen, R., and C. O'Leary. "Human Efficiency in Numeral Recognition." Perception 26, no. 1_suppl (August 1997): 289. http://dx.doi.org/10.1068/v970061.

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Using a forced-choice method, we determined human contrast thresholds for recognising handwritten numerals. Digitised numerals were presented on a computer display with additive white static noise. The numerals were either unfiltered or were filtered to two-octave spatial-frequency bands of different centre frequencies varying from 1.2 to 17.7 cycles/object height. We had ten variations of each numeral representing the handwriting of different persons. Human performance was compared with the performance of an ideal ‘signals-known-exactly’ (template matching) observer, and the results were presented in terms of efficiency. The highest efficiency for the band-pass filtered numerals was about 11% at centre frequencies of 3 – 5 cycles/object. The efficiency declined towards lower and higher centre frequencies so that at 1.2 cycles/object and 18 cycles/object the efficiency was about 4%. The efficiencies for unfiltered numerals were about 10% – 14%, being thus slightly higher than or equal to the highest efficiency of the band-pass filtered numerals. If only a two-octave band of spatial frequencies contributed character recognition, as has been suggested previously, the unfiltered numerals would contain redundant low-frequency and high-frequency information. Band-pass filtered numerals of optimal centre frequency would contain less redundancy, and a larger proportion of contrast energy would be used. Therefore, efficiency for them should have been higher than for unfiltered numerals. Since this was not the case, it seems that human observers are able to use a relatively broad band of spatial frequencies in character recognition.

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

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

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YEUNG, DANIEL S., HING-YIP CHAN, and KWAN-FAI CHEUNG. "INCORPORATING PRODUCTION RULES WITH SPATIAL INFORMATION ONTO A NEOCOGNITRON NEURAL NETWORK." International Journal of Neural Systems 05, no. 02 (June 1994): 131–42. http://dx.doi.org/10.1142/s0129065794000153.

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Rule-embedded neocognitron (REN) is proposed where the knowledge base of a neocognitron is constructed through incorporating production rules into its interlayer connections. Prototype patterns training is not required. The semantic of interlayer connections is established. The resulting network can now be analyzed according to the rule structure and problematic portions can be corrected. We demonstrate the ease with which performance can be improved by applying REN on handwritten numeral recognition. The same set of handwritten numerals initiated by Fukushima is used to test this methodology. It is found that the performance is comparable with that of Fukushima’s neocognitron with supervised training.

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Kaldan, Tenzin, and Adiyillam Vijayalakshmi. "TenzinNet for handwritten Tibetan numeral recognition." International Journal of Information Technology 13, no. 4 (May 24, 2021): 1679–82. http://dx.doi.org/10.1007/s41870-021-00711-0.

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Huang, Jun S., and Keren Chuang. "Heuristic approach to handwritten numeral recognition." Pattern Recognition 19, no. 1 (January 1986): 15–19. http://dx.doi.org/10.1016/0031-3203(86)90027-0.

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Benchaou, Soukaina, M’Barek Nasri, and Ouafae El Melhaoui. "New Approach of Features Extraction for Numeral Recognition." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (May 9, 2016): 1650014. http://dx.doi.org/10.1142/s0218001416500142.

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This paper proposes a new approach of features extraction based on structural and statistical techniques for handwritten, printed and isolated numeral recognition. The structural technique is inspired from the Freeman code, it consists first of contour detection and closing it by morphological operators. After that, the Freeman code was applied by extending its directions to 24-connectivity instead of 8-connectivity. Then, this technique is combined with the statistical method profile projection to determine the attribute vector of the particular numeral. Numeral recognition is carried out in this work through k-nearest neighbors and fuzzy min-max classification. The recognition rate obtained by the proposed system is improved indicating that the numeral extracted features contain more details.

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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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Das, Mamatarani, Mrutyunjaya Panda, and Shreela Dash. "Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition." Advances in Multimedia 2022 (December 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/6180701.

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The performance of any machine learning model largely depends on the type of input data provided. The higher the volume and variety of the data, the better the machine learning models get trained, thereby producing more accurate results. However, it is a challenging task to get high volume of data in some cases containing enough variety. Handwritten character recognition for Odia language is one of them. NITROHCS v1.0 for handwritten Odia characters and the ISI image database for handwritten Odia numerals are the standard Odia language datasets available for the research community. This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. These models, with the augmentation techniques discussed in the paper, even lead to a further increase in accuracy by approximately 1% across the models. The claims are supported by the results from the experiments done on the proposed convolutional neural network models on standard available Odia character and numeral data set.

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Al-Hmouz, Ahmad, Ghazanfar Latif, Jaafar Alghazo, and Rami Al-Hmouz. "Enhanced Numeral Recognition for Handwritten Multi-language Numerals Using Fuzzy Set-Based Decision Mechanism." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 99–107. http://dx.doi.org/10.18178/ijmlc.2020.10.1.905.

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SRIHARI, SARGUR N. "HANDWRITTEN ADDRESS INTERPRETATION: A TASK OF MANY PATTERN RECOGNITION PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 05 (August 2000): 663–74. http://dx.doi.org/10.1142/s0218001400000441.

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A gradation of pattern discrimination problems is encountered in interpreting handwritten postal addresses. There are several multiclass discrimination problems, including handwritten numeral recognition with 10 classes, alphanumeral recognition with 36 classes, and touching-digit pair recognition with 100 classes. Word recognition with a lexicon is a problem where the number of classes varies from a few to about a thousand. Some of the discrimination techniques, particularly those with few classes, lend themselves well to neural network classification, while others are better handled by Bayesian polynomial and nearest-neighbor methods. This paper describes each of the discrimination problems and the performances of each of the subsystems in a handwritten address interpretation system developed at CEDAR.

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HN, Ashoka, Manjaiah D H, and Rabindranath Bera. "Kannada Handwritten numeral Recognition using FFBPNN Classifiers." International Journal of Computer Applications 91, no. 5 (April 18, 2014): 17–21. http://dx.doi.org/10.5120/15877-4838.

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Wen, Ying, and Lianghua He. "A classifier for Bangla handwritten numeral recognition." Expert Systems with Applications 39, no. 1 (January 2012): 948–53. http://dx.doi.org/10.1016/j.eswa.2011.07.092.

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Hu, Kai Hua, Bing Xiang Liu, and Yu Jing Zhang. "Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition." Applied Mechanics and Materials 190-191 (July 2012): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.261.

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This article is based on Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition on an image. Under the precondition of the clustering numbers are known and a adequate treatment process was developed for the image, we carry through cluster analysis to digital image by experiment. The article elaborates on the basic concepts and the algorithm’s principle of Ant Colony Clustering Algorithm. The workflow to the algorithmic flow of Ant Colony Clustering Algorithm will be elaborated in the following chapters. The paper also detailed discuss the implement of the algorithm.

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Bhatti, Aamna, Ameera Arif, Waqar Khalid, Baber Khan, Ahmad Ali, Shehzad Khalid, and Atiq ur Rehman. "Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques." Applied Sciences 13, no. 3 (January 27, 2023): 1624. http://dx.doi.org/10.3390/app13031624.

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Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is written in opposite direction which induces complexities in the recognition process. This paper presents the recognition and classification of a novel Urdu numeral dataset using convolutional neural network (CNN) and its variants. We propose custom CNN model to extract features which are used by Softmax activation function and support vector machine (SVM) classifier. We compare it with GoogLeNet and the residual network (ResNet) in terms of performance. Our proposed CNN gives an accuracy of 98.41% with the Softmax classifier and 99.0% with the SVM classifier. For GoogLeNet, we achieve an accuracy of 95.61% and 96.4% on ResNet. Moreover, we develop datasets for handwritten Urdu numbers and numbers of Pakistani currency to incorporate real-life problems. Our models achieve best accuracies as compared to previous models in the literature for optical character recognition (OCR).

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Zhang, Lei, Lili Duan, and Jiasun Suo. "Real time handwritten digital image recognition system based on edge computing equipment." ITM Web of Conferences 47 (2022): 02026. http://dx.doi.org/10.1051/itmconf/20224702026.

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With the development of edge computing technology, real-time handwritten numeral recognition system deployed in edge computing devices has a bright future. However, the edge computing equipment has weak computing power and limited storage space, so the mainstream image recognition neural network can not guarantee the real-time performance on low-performance edge devices. To solve this problem, this paper designs a handwritten digit recognition system which is suitable for low performance edge computing devices. The system extracts the effective information such as the position of the number in the input image through the image preprocessing module, and infers through a lightweight neural network specially designed for edge devices. In this paper, the proposed handwritten numeral image recognition system is deployed on the edge computing device Jetson Nano. The experimental data show that the inference speed of our model is 10 times faster than that of the original Tensorflow inference, and 60 times higher than the neural network Mobilenet specially designed for mobile devices. At the same time, with the increase of input video resolution, the FPS of the system does not decline significantly, which can meet the needs of most edge tasks. Finally, the system also provides design and deployment experience for other edge AI tasks.

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BHATTACHARYA, UJJWAL, TANMOY KANTI DAS, AMITAVA DATTA, SWAPAN KUMAR PARUI, and BIDYUT BARAN CHAUDHURI. "A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 845–64. http://dx.doi.org/10.1142/s0218001402002027.

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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

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Jiang, Qisheng. "A Financial Handwritten Digit Recognition Model Based on Artificial Intelligence." Frontiers in Computing and Intelligent Systems 2, no. 2 (January 2, 2023): 70–74. http://dx.doi.org/10.54097/fcis.v2i2.4146.

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Handwritten digit recognition is a kind of image information classification problem through optical character analysis. Its essence is to represent the pixel points of digital images as gray values and then replace the pixel matrix with a numerical matrix. The computer can deal with numerical problems converted from handwritten Arabic numeral recognition problems through feature extraction and classification. With the rapid development of science and technology, this technology has dramatically reduced the cost of identification and human consumption, making identification more efficient and having a specific use value. However, the current handwritten digit recognition technology will cause problems such as abnormal recognition and recognition errors, reducing recognition accuracy. This will not only increase the cost of human recognition but also increase unnecessary risks. Based on the broad application prospect of handwritten digit recognition in finance, this paper focuses on the research and analysis of the handwritten digit recognition model for its insufficient accuracy, low performance, and other problems. Based on traditional data analysis, this paper adopts deep learning and control variable methods to conduct multiple groups of experiments to explore the impact of different parameters on the accuracy of experimental results. Summarize the best recognition accuracy and achieve the best model performance.

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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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Sajedi, Hedieh, and Mehran Bahador. "Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines." International Journal of Computational Intelligence and Applications 15, no. 01 (March 2016): 1650004. http://dx.doi.org/10.1142/s1469026816500048.

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In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

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Kumar Prasad, Binod. "APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION." International Journal of Students' Research in Technology & Management 9, no. 2 (April 13, 2021): 7–12. http://dx.doi.org/10.18510/ijsrtm.2021.922.

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Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time. Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox. Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures. Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc. Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.

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Shrivastava, Shailedra Kumar, and Sanjay S. Gharde. "Support Vector Machine for Handwritten Devanagari Numeral Recognition." International Journal of Computer Applications 7, no. 11 (October 10, 2010): 9–14. http://dx.doi.org/10.5120/1293-1769.

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KJasim, Mahmood, Anwar M Al-Saleh, and Alaa Aljanaby. "A Fuzzy Logic based Handwritten Numeral Recognition System." International Journal of Computer Applications 83, no. 10 (December 18, 2013): 36–43. http://dx.doi.org/10.5120/14487-2796.

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Singh, Pratibha, Ajay Verma, and Narendra S. Chaudhari. "Devanagri Handwritten Numeral Recognition using Feature Selection Approach." International Journal of Intelligent Systems and Applications 6, no. 12 (November 8, 2014): 40–47. http://dx.doi.org/10.5815/ijisa.2014.12.06.

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Singh, Pritpal, and Sumit Budhiraja. "Offline Handwritten Gurmukhi Numeral Recognition using Wavelet Transforms." International Journal of Modern Education and Computer Science 4, no. 8 (August 14, 2012): 34–39. http://dx.doi.org/10.5815/ijmecs.2012.08.05.

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Rumma, Shivanand, Vishweshwarayya C. H, and Bhuvaneshwari B. D. "Handwritten Kannada Numeral Recognition using Radial Basis Function." International Journal of Computer Applications 98, no. 8 (July 18, 2014): 18–20. http://dx.doi.org/10.5120/17204-7417.

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Hou, Jinhui, Huanqiang Zeng, Lei Cai, Jianqing Zhu, Jing Chen, and Canhui Cai. "Multi-task learning network for handwritten numeral recognition." Journal of Intelligent & Fuzzy Systems 36, no. 2 (March 16, 2019): 843–50. http://dx.doi.org/10.3233/jifs-169862.

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Prabhanjan, S., and R. Dinesh. "Handwritten Devanagari Numeral Recognition by Fusion of Classifiers." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 7 (July 31, 2015): 41–50. http://dx.doi.org/10.14257/ijsip.2015.8.7.05.

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Journal articles on the topic 'Handwritten numeral recognition'

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