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BV Subba Rao, Et al. "Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 344–59. http://dx.doi.org/10.17762/ijritcc.v11i10.8497.
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Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.
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Rasika, R. Janrao *. Mr. D. D. Dighe. "HANDWRITTEN ENGLISH CHARACTER RECOGNITION USING LVQ AND KNN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 904–12. https://doi.org/10.5281/zenodo.60830.
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A Handwritten character recognition (HCR) is an important task of detecting and recognizing in characters from the input digital image and convert it to other equivalent machine editable form. It gives high growth in image processing and pattern recognition. It has big challenges in data interpretation from language identification, bank cheques and conversion of any handwritten document into structural text form. Handwritten character recognition system uses a soft computing method like neural network, having area of research for long time with multiple theories and developed algorithm. Feature Extraction done in character recognition by introducing a new approach, diagonal based feature extraction. We used two Dataset, first one is own database of 26 alphabets, 10 numbers and 5 special characters written by various people and second is standard CEDAR database. The character recognition is carried out by supervised KNN classifier and LVQ. The results show that KNN has better results than LVQ.
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Shital, Bagal, Deore Vaishnavi, Adsare Samiksha, Thube Shreya, and Bhandakkar M.P. "HAND WRITTEN CHARACTER RECOGNITION USING DEEP NEURAL NETWORK." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 233–37. https://doi.org/10.5281/zenodo.15180414.
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ABSTRACTHandwritten character recognition (HCR) is a challenging problem in the field of computer vision,with numerous applications in automated data entry, postal sorting, and document digitization. Thispaper presents an approach for handwritten character recognition using deep neural networks,specifically employing a Convolutional Neural Network (CNN) algorithm. The CNN model is trainedon a comprehensive dataset of handwritten characters, and it automatically learns and extracts relevantfeatures from the input images. Through various layers of convolution, pooling, and fully connectednetworks, the system achieves high accuracy in recognizing characters by capturing intricate detailssuch as stroke patterns and shapes. The proposed method significantly improves recognition accuracycompared to traditional machine learning approaches by utilizing CNN's capacity for feature extractionand pattern recognition. The model's performance is validated through experiments on standardbenchmark datasets, showing superior results in terms of precision, recall, and F1 score. Key Words: Handwritten Character Recognition, Convolutional Neural Network, Deep NeuralNetworks, Image Processing, Pattern Recognition, Feature Extraction, Machine Learning.
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VINITA, PATIL. "Modeling of Hybrid CNN Framework for Handwritten Character and Digit Recognition System." Advanced Engineering Science 54, no. 2 (2023): 661–73. https://doi.org/10.5281/zenodo.7890787.
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Purpose: In this paper highlights the recognition of hand-written Content/character problems that have been proposed. This study shows the emerging and widely used in many vision-based applications in recent years. In this work, the drawback of conventional handwriting recognition utilizes the statistical characteristics of individual Content/characters, and different subsets to recognize the different Content/characters are overcome. Design/Methodology/Approach: To mitigate this problem several works have been motivated to incorporate a deep learning model for handwriting recognition and achieved significant performance improvement in recent times. In deep learning models, Convolutional neural networks (CNNs) are providing promising solutions to automate the distinct feature extraction from all given training samples. In this work pre-processing measures have been used to explore the boundary details of input samples for CNN-based handwritten character and digit recognition systems. Here, we proposed a hybrid novel CNN model which combines the detailed feature extraction for final classification. The proposed CNN framework is trained using the Modified National Institute of Standards and Technology (MNIST) database. Findings/Result: The performance validation includes both character and digits classification from given benchmark image sets. The validation also includes some self-build handwritten characters and digits for analyzing its resistance to the real-time capture. From the experimental results, it is observed that the variation inaccuracies are significant with the number of layers used in the CNN network. Finally, to validate the performance metrics of the proposed handwritten character recognition system it is compared with the existing state-of-the-art HCR models in terms of overall recognition accuracy. Originality/Value: In this work, the drawback of conventional handwriting recognition utilizes the statistical characteristics of individual Content/characters, and different subsets to recognize the different Content/characters are overcome. The objectives for accommodating handwritten content/characters written in different formats are difficult to accomplish irrespective of the methodologies used for classification.
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Gassi, Sajad Ahmad, Ravinder Pal Singh, and Dr Monika Mehra. "Real Time Character Recognition using Convolution Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1156–62. http://dx.doi.org/10.22214/ijraset.2022.47540.
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Abstract: Handwritten recognition of character (HCR) is a significant element in the current world and one of the focused fields in image processing and pattern recognition research. Handwritten recognition of character refers to the process of converting hand-written character into printed/word file character that in many applications may greatly enhance the interaction of man and machine. The styles, varied sizes and orientation angles of the current characters are tough to parse with large variances. In addition, it is hard to split cursive handwritten text as the edges cannot be clearly seen. Many ways of recognizing handwritten data are available. The proposed research is based on 5*5 convolution neural network where the performance of the system has been enhanced in terms of accuracy, precision and recall the data set. The research utilized the real time photos. The processing approaches are followed by binarization, skeletonisation, dilution, resizing, segmentation and extraction. The character characteristics are sent to CNN to train the models after preprocessing. The research achieved 92% accuracy and time delay while detecting the real time Images.
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Hamdan, Yasir Babiker, and Sathish. "Construction of Statistical SVM based Recognition Model for Handwritten Character Recognition." June 2021 3, no. 2 (2021): 92–107. http://dx.doi.org/10.36548/jitdw.2021.2.003.
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There are many applications of the handwritten character recognition (HCR) approach still exist. Reading postal addresses in various states contains different languages in any union government like India. Bank check amounts and signature verification is one of the important application of HCR in the automatic banking system in all developed countries. The optical character recognition of the documents is comparing with handwriting documents by a human. This OCR is used for translation purposes of characters from various types of files such as image, word document files. The main aim of this research article is to provide the solution for various handwriting recognition approaches such as touch input from the mobile screen and picture file. The recognition approaches performing with various methods that we have chosen in artificial neural networks and statistical methods so on and to address nonlinearly divisible issues. This research article consisting of various approaches to compare and recognize the handwriting characters from the image documents. Besides, the research paper is comparing statistical approach support vector machine (SVM) classifiers network method with statistical, template matching, structural pattern recognition, and graphical methods. It has proved Statistical SVM for OCR system performance that is providing a good result that is configured with machine learning approach. The recognition rate is higher than other methods mentioned in this research article. The proposed model has tested on a training section that contained various stylish letters and digits to learn with a higher accuracy level. We obtained test results of 91% of accuracy to recognize the characters from documents. Finally, we have discussed several future tasks of this research further.
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Neha*1, &. Deepti Ahlawat2. "HANDWRITTEN ALPHANUMERIC CHARACTER RECOGNITION AND COMPARISON OF CLASSIFICATION TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 419–28. https://doi.org/10.5281/zenodo.1147604.
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Several techniques have been proposed by many researchers for handwritten as well as printed character and numerals recognition. Recognition is the process of conversion of handwritten text into machine readable form. To achieve the best accuracy of any recognition system the selection of feature extraction and classification technique is important. The data about the character is collected by the features and accordingly classifiers classify the character uniquely. For handwritten characters there are drawbacks like it differs from one writer to another, even when same person writes same character a number of times there is difference in shape, size and position of character. Latest research in this area have used various types of method, classifiers and features to reduce complexity of recognizing handwritten text. In this paper, advantages and disadvantages of two different techniques of feature extraction and classification have been discussed.
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Li, Lei, Xue Gao, and Lianwen Jin. "HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm." Scientific Programming 2018 (September 5, 2018): 1–16. http://dx.doi.org/10.1155/2018/6509275.
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Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.
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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering Research and Science 3, no. 7 (2018): 67. http://dx.doi.org/10.24018/ejers.2018.3.7.831.
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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering and Technology Research 3, no. 7 (2018): 67–73. http://dx.doi.org/10.24018/ejeng.2018.3.7.831.
Full textAbstract:
Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
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V M, Praseetha, and Joby P P. "Enhancing Handwritten Text Recognition on Mobile Platforms Using Cloudlet-Assisted Deep Learning." Journal of Innovative Image Processing 6, no. 4 (2025): 456–71. https://doi.org/10.36548/jiip.2024.4.008.
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The limited computational resources of mobile devices significantly constrain their ability to effectively execute resource-intensive computer vision tasks, such as , video processing, augmented reality, and face recognition. Handwritten character recognition (HCR) is a critical application of machine learning and computer vision, with wide-ranging implications for digital transformation and accessibility. This proposed study explores the implementation of a deep learning-based HCR system tailored for mobile phones using cloudlet. A cloudlet is a novel technology aimed at boosting the computational power of mobile devices to manage resource-intensive tasks. It enables mobile clients to interact with nearby servers, allowing only minimally processed data to be transmitted from the mobile application to the server. The server handles the complex computations and sends the results back to the mobile device in real-time. By utilizing Convolutional Neural Networks (CNNs), the proposed method achieves high accuracy and efficiency on resource-constrained mobile devices. The research details the system architecture, dataset preprocessing, model training, and integration into an Android application, offering a robust solution for real-time handwritten character recognition.
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XU, RUIFENG, DANIEL YEUNG, WENHAO SHU, and JIAFENG LIU. "A HYBRID POST-PROCESSING SYSTEM FOR HANDWRITTEN CHINESE CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 06 (2002): 657–79. http://dx.doi.org/10.1142/s0218001402001964.
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In this paper, a hybrid post-processing system for improving the performance of Handwritten Chinese Character Recognition is presented. In order to remove two kinds of frequently encountered errors in the recognition result, namely mis-recognized character and unrecognized character, both confusing character characteristics of the recognizer and the contextual linguistic information are utilized in our hybrid three-stage post-processing system. In the first stage, the confusing character set and a statistical Noisy-Channel model are employed to identify the most promising candidate character and append possible unrecognized similar-shaped characters into candidate character set when a candidate sequence is given. Secondly, dictionary-based approximate word matching is conducted to further append contextual linguistic-prone characters into candidate character set and bind the candidate characters into a word-lattice. Finally, a Chinese word BI-Gram Markov model is employed in the third stage to identify a most promising sentence by selecting plausible words from the word-lattice. On the average, our system achieves a 5.1% recognition rate improvement for the first candidate when the original character recognition rate is 90% for the first candidate and 95% for the top-10 candidates by an online HCCR engine.
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Chacko, Binu P. "Comparison of Feature Extraction Techniques for Pattern Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5511–17. http://dx.doi.org/10.22214/ijraset.2021.36214.
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Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.
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Yingna, Zhong, Kauthar Mohd Daud, Kohbalan Moorthy, and Ain Najiha Mohamad Nor. "Filtering Approaches and Mish Activation Function Applied on Handwritten Chinese Character Recognition." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 13 (November 1, 2024): e31218. https://doi.org/10.14201/adcaij.31218.
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Handwritten Chinese Characters (HCC) have recently received much attention as a global means of exchanging information and knowledge. The start of the information age has increased the number of paper documents that must be electronically saved and shared. The recognition accuracy of online handwritten Chinese characters has reached its limit as online characters are more straightforward than offline characters. Furthermore, online character recognition enables stronger involvement and flexibility than offline characters. Deep learning techniques, such as convolutional neural networks (CNN), have superseded conventional Handwritten Chinese Character Recognition (HCCR) solutions, as proven in image identification. Nonetheless, because of the large number of comparable characters and styles, there is still an opportunity to improve the present recognition accuracy by adopting different activation functions, including Mish, Sigmoid, Tanh, and ReLU. The main goal of this study is to apply a filter and activation function that has a better impact on the recognition system to improve the performance of the recognition CNN model. In this study, we implemented different filter techniques and activation functions in CNN to offline Chinese characters to understand the effects of the model's recognition outcome. Two CNN layers are proposed given that they achieve comparative performances using fewer-layer CNN. The results demonstrate that the Weiner filter has better recognition performance than the median and average filters. Furthermore, the Mish activation function performs better than the Sigmoid, Tanh, and ReLU functions.
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Chang, Yun, Jia Lee, Omar Rijal, and Syed Bakar. "Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model." International Journal of Applied Mathematics and Computer Science 20, no. 4 (2010): 727–38. http://dx.doi.org/10.2478/v10006-010-0055-x.
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Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship modelThis paper presents novel feature extraction and classification methods for online handwritten Chinese character recognition (HCCR). TheX-graph andY-graph transformation is proposed for deriving a feature, which shows useful properties such as invariance to different writing styles. Central to the proposed method is the idea of capturing the geometrical and topological information from the trajectory of the handwritten character using theX-graph and theY-graph. For feature size reduction, the Haar wavelet transformation was applied on the graphs. For classification, the coefficient of determination (R2p) from the two-dimensional unreplicated linear functional relationship model is proposed as a similarity measure. The proposed methods show strong discrimination power when handling problems related to size, position and slant variation, stroke shape deformation, close resemblance of characters, and non-normalization. The proposed recognition system is applied to a database with 3000 frequently used Chinese characters, yielding a high recognition rate of 97.4% with reduced processing time of 75.31%, 73.05%, 58.27% and 40.69% when compared with recognition systems using the city block distance with deviation (CBDD), the minimum distance (MD), the compound Mahalanobis function (CMF) and the modified quadratic discriminant function (MQDF), respectively. High precision rates were also achieved.
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Ampelakiotis, Vaios, Isidoros Perikos, Ioannis Hatzilygeroudis, and George Tsihrintzis. "Optical Recognition of Handwritten Logic Formulas Using Neural Networks." Electronics 10, no. 22 (2021): 2761. http://dx.doi.org/10.3390/electronics10222761.
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In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.
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XU, RUIFENG, DANIEL S. YEUNG, and DAMING SHI. "A HYBRID POST-PROCESSING SYSTEM FOR OFFLINE HANDWRITTEN CHINESE CHARACTER RECOGNITION BASED ON A STATISTICAL LANGUAGE MODEL." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (2005): 415–28. http://dx.doi.org/10.1142/s0218001405004046.
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This paper presents a post-processing system for improving the recognition rate of a Handwritten Chinese Character Recognition (HCCR) device. This three-stage hybrid post-processing system reduces the misclassification and rejection rates common in the single character recognition phase. The proposed system is novel in two respects: first, it reduces the misclassification rate by applying a dictionary-look-up strategy that bind the candidate characters into a word-lattice and appends the linguistic-prone characters into the candidate set; second, it identifies promising sentences by employing a distant Chinese word BI-Gram model with a maximum distance of three to select plausible words from the word-lattice. These sentences are then output as the upgraded result. Compared with one of our previous works in single Chinese character recognition, the proposed system improves absolute recognition rates by 12%.
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Alom, Md Zahangir, Paheding Sidike, Mahmudul Hasan, Tarek M. Taha, and Vijayan K. Asari. "Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks." Computational Intelligence and Neuroscience 2018 (August 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/6747098.
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In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.
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Panyam, Narahari Sastry, Sameer Syed, and Sameer Syed Mohammed. "Recognition of Offline Handwritten Characters using 2D-FFT for English and Hindi Scripts." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1512–17. https://doi.org/10.35940/ijeat.D7339.049420.
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The Handwritten Character Recognition has been a challenging task for the past many decades. This is an old application related to the area of pattern recognition. Handwritten character recognition (HCR) can be classified into two types namely, Online and Offline. As per the literature survey, there are no standard databases for HCR [1] [2] [3] since there are very less number of speakers for any Indian language compared to English. Hence, the database of Indian scripts both for testing and training are to be developed in the laboratory environment. The recognition accuracy for printed / typed characters is more than 99 percent, whereas for the HCR it is around 60 percent. Hence the area of HCR is an open area of research. HCR for Indian languages is at nascent stage compared to English since they contain alphabets and also matra’s / sandhi are complex which make the recognition tougher. The freedom of the scriber in writing the script is also another challenge for achieving the better recognition accuracy. This work describes the handwritten character recognition of both Hindi and English scripts by extracting features using 2D FFT and using the Nearest Neighborhood Classifier. The best recognition accuracy for handwritten character recognition of English and Hindi languages obtained is 70%.
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Manoj, Sonkusare, and Sahu Narendra. "A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS." Advances in Vision Computing: An International Journal (AVC) 3, no. 1 (2016): 01–12. https://doi.org/10.5281/zenodo.3461522.
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Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside with their performance.
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Manoj, Sonkusare, and Sahu Narendra. "A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS." Advances in Vision Computing: An International Journal (AVC) 3, no. 1 (2016): 01–12. https://doi.org/10.5281/zenodo.3626432.
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Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside with their performance.
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MOHAMAD, MUHAMMAD ARIF, and Muhammad Aliif Ahmad. "Handwritten Character Recognition using Enhanced Artificial Neural Network." Journal of Advanced Research in Computing and Applications 36, no. 1 (2024): 1–9. http://dx.doi.org/10.37934/arca.36.1.19.
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This study addresses the concerns regarding the performance of Handwritten Character Recognition (HCR) systems, focusing on the classification stage. It is widely acknowledged that the development of the classification model significantly impacts the overall performance of HCR. The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. The objective of this study is to improve and refine the ANN classification model to achieve better HCR. To achieve this goal, this study proposed a hybrid Flower Pollination Algorithm with Artificial Neural Network (FPA-ANN) classification model for HCR. The FPA is one of the metaheuristic approaches is utilized as an optimization technique to enhance the performance of ANN, particularly by optimizing the network training process of ANN. The experimentation phase involves using the National Institute of Standards and Technology (NIST) handwritten character database. Finally, the proposed FPA-ANN classification model is analyzed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy.
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Mohamad, Muhammad Arif, Muhammad Aliif Ahmad, and Jamilah Mahmood. "Feature Extraction Algorithm based Metaheuristic Optimization for Handwritten Character Recognition." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 16, no. 2 (2024): 27–30. http://dx.doi.org/10.54554/jtec.2024.16.02.004.
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Interest in feature extraction for Handwritten Character Recognition (HCR) has been growing due to numerous algorithms aimed at improving classification accuracy. This study introduces a metaheuristic approach utilizing the Honey Badger Algorithm (HBA) for feature extraction in HCR. The Freeman Chain Code (FCC) is employed for data representation. One challenge with using FCC to represent characters is that extraction results vary depending on the starting points, affecting the chain code's route length. To address this issue, a metaheuristic approach using HBA is proposed to identify the shortest route length and minimize computational time for HCR. The performance metrics of the HB-FCC extraction algorithm are route length and computation time. Experiments on the algorithm use chain code representations from the Center of Excellence for Document Analysis and Recognition (CEDAR) dataset, containing 126 uppercase letter characters. According to the results, the proposed HB-FCC method achieves a route length of 1880.28 and requires only 1.07 seconds to process the entre set of character images.
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Tirapathi Reddy B. "Handwritten Character Recognition System." Journal of Electrical Systems 20, no. 3 (2024): 1465–75. http://dx.doi.org/10.52783/jes.3553.
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Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection of handwritten characters gathered under various writing styles, sizes, and orientations in order to guarantee the durability and generalization power of the model. To enhance its quality and diversity, the training data is put through a rigorous preparation procedure that includes picture augmentation, noise removal, and normalization. The studies' results demonstrate how well and precisely the proposed system can recognize handwritten characters in a range of languages and writing styles. The system performs competitively compared to state-of-the-art methods and demonstrates robustness against variations in handwriting style and quality. Furthermore, the system has potential in terms of efficiency and scalability, making it suitable for real-time applications such as document digitalization, handwritten word recognition in electronic devices, and automatic form processing.
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Ritonga, Mahyudin, Manoj L. Bangare, Pushpa Manoj Bangare, et al. "Optimized convolutional neural network deep learning for Arabian handwritten text recognition." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1497–506. https://doi.org/10.11591/eei.v14i2.7696.
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In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.
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APARNA, S. MENON, and L. KALA. "HANDWRITTEN CHARACTER RECOGNITION OF SIMILAR LOOKING LETTERS USING ANGULAR FEATURE EXTRACTION METHOD." JournalNX - A Multidisciplinary Peer Reviewed Journal 4, no. 7 (2018): 1–4. https://doi.org/10.5281/zenodo.1472719.
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Classification of similar looking handwritten characters in Indian languages is challenging. Especially in the case of Malayalam, a Dravidian language from the Brahmic script which is characterized by its highly curvy and looped nature. So by considering such a nature of the language, a new feature extraction method in the field of HCR (Handwritten Character Recognition) named as Angular Method is used by using fuzzy logic classifiers with k –NN algorithm. https://journalnx.com/journal-article/20150764
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Mujadded, Al Rabbani Alif. "State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture." State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture 9, no. 1 (2024): 11. https://doi.org/10.5281/zenodo.10538255.
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Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics further differ based on factors such as the writer's age and birthplace. In this paper, we propose a modified ResNet-34 architecture, a convolutional neural network (CNN) model, to identify Bangla handwritten characters accurately. The proposed approach is validated using a merged subset of two popular Bangla handwritten datasets. Through our technique, we achieve state-of-the- art recognition performance. Experimental results demonstrate that the suggested model attains an average accuracy of 98.70% for Bangla handwritten vowels, 97.34% for consonants, and 99.02% for numeric characters. Additionally, when applied to a mixed dataset comprising vowels, consonants, and numeric characters, the proposed model achieves an overall accuracy of 97%. This research contributes to advancing digital manufacturing systems by addressing the challenge of Bangla Handwritten Character Recognition, offering a high-performing solution based on a modified ResNet-34 architecture. The achieved recognition accuracy signifies significant progress in this field, potentially paving the way for enhanced automation and efficiency in various applications that involve processing Bangla handwritten text. Keywords:- Handwritten Character Recognition; ResNet; Optical Character Recognition; Computer Vision; Convolutional Neural Networks.
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Shrivastav, Jitendra, Ravindra Kumar Gupta, and Shailendra Singh. "A Modified Back propagation Algorithm for Optical Character Recognition." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 06 (2013): 180–84. https://doi.org/10.5281/zenodo.14605792.
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Character Recognition (CR) has been an active area of research and due to its diverse applicable environment; it continues to be a challenging research topic. There is a clear need for optical character recognition in order to provide a fast and accurate method to search both existing images as well as large archives of existing paper documents. However, existing optical character recognition programs suffer from a flawed tradeoff between speed and accuracy, making it less attractive for large quantities of documents. In this thesis, we present a new neural network based method for optical character recognition as well as handwritten character recognition. Experimental results show that our proposed method achieves highest percent accuracy in optical character recognition. We present an overview of existing handwritten character recognition techniques. All these algorithms are described more or less on their own. Handwritten character recognition is a very popular and computationally expensive task. We also explain the fundamentals of handwritten character recognition. We describe today’s approaches for handwritten character recognition. From the broad variety of efficient techniques that have been developed we will compare the most important ones. We will systematize the techniques and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
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Anang Aris Widodo, Muchammad Yuska Izza Mahendra, and Mohammad Zoqi Sarwani. "Recognition of Korean Alphabet (Hangul) Handwriting into Latin Characters Using Backpropagation Method." International Journal of Artificial Intelligence & Robotics (IJAIR) 3, no. 2 (2021): 50–57. http://dx.doi.org/10.25139/ijair.v3i2.4210.
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The popularity of Korean culture today attracts many people to learn everything about Korea, especially in learning the Korean language. To learn Korean, you must first know Korean letters (Hangul), which are non-Latin characters. Therefore, a digital approach is needed to recognize handwritten Korean (Hangul) words easily. Handwritten character recognition has a vital role in pattern recognition and image processing for handwritten Character Recognition (HCR). The backpropagation method trains the network to balance the network's ability to recognize the patterns used during training and the network's ability to respond correctly to input patterns that are similar but not the same as the patterns used during training. This principle is used for character recognition of Korean characters (Hangul), a sub-topic in fairly complex pattern recognition. The results of the calculation of the backpropagation artificial neural network with MATLAB in this study have succeeded in identifying 576 image training data and 384 Korean letter testing data (Hangul) quite well and obtaining a percentage result of 80.83% with an accuracy rate of all data testing carried out on letters. Korean (Hangul).
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Santosh, Acharya, Dhungel Shashank, and Kr. Jha Ashish. "Nepali Handwritten Character Recognition System." Advancement in Image Processing and Pattern Recognition 5, no. 3 (2022): 1–6. https://doi.org/10.5281/zenodo.7472398.
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Even if the technological and digital world is expanding more quickly, there are still many things that are lacking. What a wonderful thing it would be to be able to trust machines to scan any handwritten characters into digital representation. The method for doing this is called optical character recognition (OCR), but there is still much room for improvement. Although there has been work done on it, the technique developed for one language cannot be applied to another due to language variations. Nepali is not a language that is frequently used online. Perhaps this is why there are fewer OCR systems developed using this language. We have made an effort to improve on it so that Nepali characters can be recognized. Basically, the idea is to use a camera to scan Nepali handwriting from hard copy paper, locate the regions in the image where the characters are present, segment those localized parts into characters, and then digitally display each predicted segmented character.
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Sonkusare, Manoj, and Narendra Sahu. "A Survey on Handwritten Character Recognition (HCR) Techniques for English Alphabets." Advances in Vision Computing: An International Journal 3, no. 1 (2016): 1–12. http://dx.doi.org/10.5121/avc.2016.3101.
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C. S. Pillai, Shakunthala B. S. ,. Ullas H. S. ,. "Kannada Handwritten Word Recognition Using Esld Method." Tuijin Jishu/Journal of Propulsion Technology 44, no. 6 (2023): 5444–52. http://dx.doi.org/10.52783/tjjpt.v44.i6.4988.
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The HCR method normally identifies writers' identities. It includes the crucial phase of segmentation, in which the handwriting text is converted into lines. SLD and the R Clustering technique for line and character segmentation in the framework of a handwritten documents Kannada literature. The words from the text lines are arranged here using an intelligent technique. The words that have been detected are then retrieved and saved in a new picture. During extraction, it is assured that no words intersect and that unnecessary information is effectively removed. Other proposed and well tested approaches to speed up the process of text region segmentation and skew repair of Kannada handwritten manuscripts are given in the investigation. The claimed average segmentation accuracy is 97.13 percent
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Upadhyay, Jitendrakumar B. "BUILT A DATASET OF GUJARATI ISOLATED HANDWRITTEN CHARACTERS AND RECOGNITION THROUGH DEEP LEARNING." international journal of advanced research in computer science 16, no. 1 (2025): 42–47. https://doi.org/10.26483/ijarcs.v16i1.7182.
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In the current era with the rise of new machine learning algorithms, particularly deep learning, the demand for large, high-quality datasets has grown significantly, especially in handwritten character recognition (HCR). While several Indian languages have publicly available benchmark datasets, a few, including Gujarati, still lack such resources. This paper addresses an attempt to build a dataset for Gujarati isolated handwritten characters and to recognize the isolated Gujarati handwritten vowels and consonants. The dataset is collected from 692 writers of varying ages, genders, qualifications, and professions. The dataset consists of 63,664 samples for 46 classes including 34 consonants and 12 vowels where 1384 images of each character. The proposed model was run with an 80:20 training and testing ratio, using 7, 10, 20, 30, & 40 epochs. The model showed promising results and achieved the highest training accuracy 90.92%, and the highest testing accuracy 89.51%.
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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 (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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Abhale, Poonam Bhanudas. "Handwritten English Alphabet Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (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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Zhong, Yingna, Kauthar Mohd Daud, Ain Najiha Binti Mohamad Nor, Richard Adeyemi Ikuesan, and Kohbalan Moorthy. "Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 4 (2023): 567–75. http://dx.doi.org/10.20965/jaciii.2023.p0567.
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Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.
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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 (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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Attigeri, Savitha. "Neural Network based Handwritten Character Recognition system." International Journal Of Engineering And Computer Science 7, no. 03 (2018): 23761–68. http://dx.doi.org/10.18535/ijecs/v7i3.18.
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Handwritten character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30x20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition
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Guha, Riya, Nibaran Das, Mahantapas Kundu, Mita Nasipuri, and K. C. Santosh. "DevNet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2052009. http://dx.doi.org/10.1142/s0218001420520096.
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The writing style is a unique characteristic of a human being as it varies from one person to another. Due to such diversity in writing style, handwritten character recognition (HCR) under the purview of pattern recognition is not trivial. Conventional methods used handcrafted features that required a-priori domain knowledge, which is always not feasible. In such a case, extracting features automatically could potentially attract more interests. For this, in the literature, convolutional neural network (CNN) has been a popular approach to extract features from the image data. However, state-of-the-art works do not provide a generic CNN model for character recognition, Devanagari script, for instance. Therefore, in this work, we first study several different CNN models on publicly available handwritten Devanagari characters and numerals datasets. This means that our study is primarily focusing on comparative study by taking trainable parameters, training time and memory consumption into account. Later, we propose and design DevNet, a modified CNN architecture that produced promising results, since computational complexity and memory space are our primary concerns in design.
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Mehulkumar Dalwadi. "Detection of Gujarati Handwritten Characters using Artificial Intelligence Techniques: Challenges, Opportunities, and Future Directions." Journal of Information Systems Engineering and Management 10, no. 24s (2025): 381–90. https://doi.org/10.52783/jisem.v10i24s.3913.
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The handwritten character recognition (HCR) has grown significance in document digitisation, multilingual text processing, and automated form interpretation. Among the different Indic scripts it is relatively underexplored, despite a large population of speakers. This paper provides a comprehensive review of the state of the art of Artificial Intelligence (AI) models for the detection and recognition of characters from Gujarati handwritten text. We critically review over 30 work references focusing on the benefits and limitations of traditional machine learning (ML) methods, hybrid models and state-of-the-art deep learning (DL) architectures. We study the unique features of the Gujarati Script including diacritical marks and visually similar subset of characters and their impact on identification quotient. In addition, we discuss important challenges such as the lack of large-scale public datasets, variations in handwriting styles, and the details of Gujarati ligatures. By synthesising current knowledge, identifying methodological gaps, and providing potential future directions, this paper aims to assist both novices and experienced researchers in designing robust, efficient, and scalable solutions for Gujarati HCR.
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Alharbi, Abir. "A Genetic-LVQ neural networks approach for handwritten Arabic character recognition." Artificial Intelligence Research 7, no. 2 (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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Haithem Abd Al-RaheemTaha. "ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM." Diyala Journal of Engineering Sciences 5, no. 1 (2012): 79–87. http://dx.doi.org/10.24237/djes.2012.05107.
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On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now, printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical, while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently, it becomes a hot topic with the release of database, which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition.
 At the realistic Arabic handwritten text recognition and explore two aspects of the problem. Firstly, a system based on segmentation-recognition integrated framework was developed for Arabic handwriting recognition. Secondly, the parameters of embedded classifier initialed at character-level training were discriminatively re-trained at string level.
 The segmentation-recognition integrated framework runs as follows: the written character is first over-segmented into primitive segments, and then the consecutive segments are combined into candidate patterns. The embedded classifier is used to classify all the candidate patterns in segmentation lattice. According to Genetic Algorithm (Crossover, mutation, and population), the system outputs the optimal path in segmentation-recognition lattice, which is the final recognition result. The embedded classifier is first trained at character level on isolated character and then the parameters are updated at string level on string samples.
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Ishan, Gulati*1 Gautam Vig2 &. Vijay Khare3. "REAL TIME HANDWRITTEN CHARACTER RECOGNITION USING ANN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 357–62. https://doi.org/10.5281/zenodo.1218609.
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<em>-</em>Real time Handwritten Character Recognition by using Template Matching is a system which is useful to recognize the character or alphabets in the given text by comparing two images of the alphabet. The objectives of this system prototype are to develop a program for the Optical Character Recognition (OCR) system by using the Template Matching algorithm . Handwritten character recognition is a challenging task in the field of research on image processing, artificial intelligence as well as machine vision since the handwriting varies from person to person. Moreover, the handwriting styles, sizes and its orientation make it even more complex to interpret the text. The numerous applications of handwritten text in reading bank cheques, Zip Code recognition and in removing the problem of handling documents manually has made it necessary to acquire digitally formatted data. This paper presents the recognition of handwritten characters using either a scanned document, or direct acquisition of image using Matlab, followed by the implementation of various other Matlab toolboxes like Image Processing and Neural Network Toolbox to process the scanned or acquired image. Experimental Results are given to present the proposed model in order to recognize handwritten characters accurately.
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Khare, Sonal, and Jaiveer Singh. "Handwritten Devanagari Character Recognition System: A Review." International Journal of Computer Applications 121, no. 9 (2015): 10–14. http://dx.doi.org/10.5120/21566-4600.
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Dadapeer, Mr. "Character Recognition System for Handwritten English Alphabets." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (2020): 1852–55. http://dx.doi.org/10.22214/ijraset.2020.30692.
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JYOTI, A.PATIL, and SANJAY R. PATIL DR. "OPTICAL HANDWRITTEN DEVNAGARI CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK APPROACH." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 3 (2018): 67–71. https://doi.org/10.5281/zenodo.1454101.
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<strong>Character recognitions play a wide role in the fast moving world with the growing technology,by providing more scope to perform research in OCR techniques. In the field of pattern recognition Devnagari handwritten character recognition is one of the challenging research area. Character recognition is defined as electronic translation of scanned images of handwritten or printed text into a machine encoded text. In this paper proposed an off line handwritten Devnagari character recognition technique with the use of feed forward neural network. For training the neural network a handwritten Devnagari character which is resized into 20x30 pixels is used. The same character is then given to the neural network as input with different set of neurons in hidden layer after the training process,and their recognition accuracy rate is calculated and compared for different Devnagari characters. Good recognition accuracy rates has been given by the proposed system comparable to that of other hand written character recognition systems.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141157</strong>
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Li, Ling Hua, Shou Fang Mi, and Heng Bo Zhang. "Template-Based Handwritten Numeric Character Recognition." Advanced Materials Research 586 (November 2012): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.586.384.
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This paper describes a stroke-based handwriting analysis method in classifying handwritten Numeric characters by using a template-based approach. Writing strokes are variable from time to time, even when the writing character is same and comes from the same user. Writing strokes include the properties such as the number of the strokes, the shapes and sizes of them and the writing order and the writing speed. We describe here a template-based system using the properties of writing strokes for the recognition of online handwritten numeric characters. Experimental results show that within the 1500 numeric characters taken from 30 writers, the system got 97.84% recognition accuracy which is better than other systems shown by other literatures.
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N D, Sukesh, and Steephan Amalraj J. "Handwritten Character Recognition Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25945.
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Handwritten digit or character recognition in transforming the printed or handwritten text from an image. Optical character recognition plays an important role in documentation scanning ,text extractions from the image. Optical character recognition is used in different fields like postal services ,Ecommerce , Shipping ,Banking sector for character extraction from the images . However the existing character recognition system faces many challenges in extracting text from noisy and distortion images or complex layout and Extraction mostly limited to numbers and English alphabets . The introduction of Deep learning has changed Optical Character Recognition by using models like Recurrent Neural Networks,convolutional neural network .In this paper i am gonna compare the different models like CNN model and CRNN model with current State of art model Transformer based Optical Character Recognition KeyWords :Transformers ,Convolution Recurrent Neural Network, Handwritten ,Optical Character Recognition
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Naidu, D. J. Samatha, and T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 8 (2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.
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Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.
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Dilmurat, Halmurat, and Kurban Ubul. "Design and Realization of On-Line Uyghur Handwritten Character Collection System." Advanced Materials Research 989-994 (July 2014): 4742–46. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4742.
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Data collection is the first step in handwritten character recognition systems, and the data quality collected effects the whole systems efficiency. As the necessary subsystem of on-line handwritten character/word recognition system, a Uyghur handwritten character collection system is designed and implemented with Visual C++ based on the nature of Uyghur handwriting. Uyghur handwritings is encoded by 8 direction tendency and stored in extension stroke file. And they are collected based on the content of Text Prompt File. From experimental results, it can be concluded that the handwriting collection system indicates its strong validity and efficiency during the collection of Uyghur handwriting.