Journal articles: 'Handwritten Character Recognition (HCR)'

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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 (November 30, 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 (June 8, 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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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 (November 30, 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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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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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 (March 30, 2016): 1–12. http://dx.doi.org/10.5121/avc.2016.3101.

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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 (July 31, 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 (July 31, 2018): 67–73. http://dx.doi.org/10.24018/ejeng.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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Guha, Riya, Nibaran Das, Mahantapas Kundu, Mita Nasipuri, and K. C. Santosh. "DevNet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (April 30, 2020): 2052009. http://dx.doi.org/10.1142/s0218001420520096.

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

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Chacko, Binu P. "Comparison of Feature Extraction Techniques for Pattern Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 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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Ampelakiotis, Vaios, Isidoros Perikos, Ioannis Hatzilygeroudis, and George Tsihrintzis. "Optical Recognition of Handwritten Logic Formulas Using Neural Networks." Electronics 10, no. 22 (November 12, 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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Saqib, Nazmus, Khandaker Foysal Haque, Venkata Prasanth Yanambaka, and Ahmed Abdelgawad. "Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data." Algorithms 15, no. 4 (April 14, 2022): 129. http://dx.doi.org/10.3390/a15040129.

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Neural networks have made big strides in image classification. Convolutional neural networks (CNN) work successfully to run neural networks on direct images. Handwritten character recognition (HCR) is now a very powerful tool to detect traffic signals, translate language, and extract information from documents, etc. Although handwritten character recognition technology is in use in the industry, present accuracy is not outstanding, which compromises both performance and usability. Thus, the character recognition technologies in use are still not very reliable and need further improvement to be extensively deployed for serious and reliable tasks. On this account, characters of the English alphabet and digit recognition are performed by proposing a custom-tailored CNN model with two different datasets of handwritten images, i.e., Kaggle and MNIST, respectively, which are lightweight but achieve higher accuracies than state-of-the-art models. The best two models from the total of twelve designed are proposed by altering hyper-parameters to observe which models provide the best accuracy for which dataset. In addition, the classification reports (CRs) of these two proposed models are extensively investigated considering the performance matrices, such as precision, recall, specificity, and F1 score, which are obtained from the developed confusion matrix (CM). To simulate a practical scenario, the dataset is kept unbalanced and three more averages for the F measurement (micro, macro, and weighted) are calculated, which facilitates better understanding of the performances of the models. The highest accuracy of 99.642% is achieved for digit recognition, with the model using ‘RMSprop’, at a learning rate of 0.001, whereas the highest detection accuracy for alphabet recognition is 99.563%, which is obtained with the proposed model using ‘ADAM’ optimizer at a learning rate of 0.00001. The macro F1 and weighted F1 scores for the best two models are 0.998, 0.997:0.992, and 0.996, respectively, for digit and alphabet recognition.

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JIN, LIANWEN, and GANG WEI. "HANDWRITTEN CHINESE CHARACTER RECOGNITION WITH DIRECTIONAL DECOMPOSITION CELLULAR FEATURES." Journal of Circuits, Systems and Computers 08, no. 04 (August 1998): 517–24. http://dx.doi.org/10.1142/s0218126698000316.

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A new feature extraction approach based on elastic meshing and directional decomposition techniques for handwritten Chinese character recognition (HCCR) is proposed in this letter. It is found that decomposing a Chinese character into horizontal, vertical stroke, left slant and right slant directional sub-patterns is very helpful for feature extraction and recognition. Three kinds of decomposition methods are proposed. A minimum distance classifier is trained by 3755 categories of characters using the new features. Testing on a total of 37,550 untrained handwritten samples produces the recognition rate of 92.36%, showing the effectiveness of the proposed approach.

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

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

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Jiang, Guoteng, Zhuang Qian, Qiu-Feng Wang, Yan Wei, and Kaizhu Huang. "Adversarial Attack and Defence on Handwritten Chinese Character Recognition." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2278/1/012023.

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Abstract Deep Neural Networks (DNNs) have shown their powerful performance in classification; however, the robustness issue of DNNs has arisen as one primary concern, e.g., adversarial attack. So far as we know, there is not any reported work about the adversarial attack on handwritten Chinese character recognition (HCCR). To this end, the classical adversarial attack method (i.e., Projection Gradient Descent: PGD) is adopted to generate adversarial examples to evaluate the robustness of the HCCR model. Furthermore, in the training process, we use adversarial examples to improve the robustness of the HCCR model. In the experiments, we utilize a frequently-used DNN model on HCCR and evaluate its robustness on the benchmark dataset CASIA-HWDB. The experimental results show that its recognition accuracy is decreased severely on the adversarial examples, demonstrating the vulnerability of the current HCCR model. In addition, we can improve the recognition accuracy significantly after the adversarial training, demonstrating its effectiveness.

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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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LI, TZE FEN, and SHIAW-SHIAN YU. "HANDPRINTED CHINESE CHARACTER RECOGNITION USING THE PROBABILITY DISTRIBUTION FEATURE." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (October 1994): 1241–58. http://dx.doi.org/10.1142/s0218001494000620.

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A simplified Bayes rule is used to classify 5401 categories of handwritten Chinese characters. The main feature for the Bayes rule deals with the probability distribution of black pixels of a thinned character. Our idea is that each Chinese character indicated by the black pixels represents a probability distribution in a two-dimensional plane. Therefore, an unknown pattern is classified into one of 5401 different distributions by the Bayes rule. Since the handwritten character has an irregular shape variation, the whole character is normalized and then thinned. Finally, a transformation is used to spread the black pixels uniformly over the whole square plane, but it still keeps the relative positions of the original black pixels. The main feature gives an 88.65% recognition rate. In order to raise the recognition rate, 4 more subsidiary features are elaborately selected such that they are not affected much by the irregularly shaped variation. The 4 features raise the recognition rate to 93.43%. A 99.30% recognition rate is achieved if the top 10 categories of HCC are selected by our recognition method and 99.61% if the top 20 are selected.

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Bi, Ning, Jiahao Chen, and Jun Tan. "The Handwritten Chinese Character Recognition Uses Convolutional Neural Networks with the GoogLeNet." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (October 2019): 1940016. http://dx.doi.org/10.1142/s0218001419400160.

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With the outstanding performance in 2014 at the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14), an effective convolutional neural network (CNN) model named GoogLeNet has drawn the attention of the mainstream machine learning field. In this paper we plan to take an insight into the application of the GoogLeNet in the Handwritten Chinese Character Recognition (HCCR) on the database HCL2000 and CASIA-HWDB with several necessary adjustments and also state-of-the-art improvement methods for this end-to-end approach. Through the experiments we have found that the application of the GoogLeNet for the Handwritten Chinese Character Recognition (HCCR) results into significant high accuracy, to be specific more than 99% for the final version, which is encouraging for us to further research.

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

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

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

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Wakahara, Toru. "Toward robust handwritten character recognition." Pattern Recognition Letters 14, no. 4 (April 1993): 345–54. http://dx.doi.org/10.1016/0167-8655(93)90100-r.

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Soselia, Davit, Magda Tsintsadze, Levan Shugliashvili, Irakli Koberidze, Shota Amashukeli, and Sandro Jijavadze. "On Georgian Handwritten Character Recognition." IFAC-PapersOnLine 51, no. 30 (2018): 161–65. http://dx.doi.org/10.1016/j.ifacol.2018.11.279.

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Bhagat, Ms Shubhangee S. "Handwritten Character Detection Using Optical Character Recognition Method." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 4724–26. http://dx.doi.org/10.22214/ijraset.2018.4775.

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Mehta, Nikita, and Jyotika Doshi. "A Review of Handwritten Character Recognition." International Journal of Computer Applications 165, no. 4 (May 17, 2017): 37–40. http://dx.doi.org/10.5120/ijca2017913855.

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Sahu, Manish Kumar, and Dr Naveen Kumar Dewangan. "A Survey on Handwritten Character Recognition." IARJSET 4, no. 1 (January 15, 2017): 89–91. http://dx.doi.org/10.17148/iarjset.2017.4120.

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Sahu, Manish Kumar, and Naveen Kumar Dewangan. "Handwritten Character Recognition using Neural Network." IJARCCE 6, no. 6 (June 30, 2017): 11–14. http://dx.doi.org/10.17148/ijarcce.2017.6603.

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Sinha, Gita, Dr Shailja Sharma, and Rakesh Kumar Roshan. "CLASSIFICATION TECHNIQUES FOR HANDWRITTEN CHARACTER RECOGNITION." International Journal of Engineering Applied Sciences and Technology 5, no. 3 (July 31, 2020): 151–57. http://dx.doi.org/10.33564/ijeast.2020.v05i03.023.

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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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Wang, Xian, Venu Govindaraju, and Sargur Srihari. "Holistic recognition of handwritten character pairs." Pattern Recognition 33, no. 12 (December 2000): 1967–73. http://dx.doi.org/10.1016/s0031-3203(99)00204-6.

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Wakahara, Toru, and Yoshimasa Kimura. "Toward robust handwritten Kanji character recognition." Pattern Recognition Letters 20, no. 10 (October 1999): 979–90. http://dx.doi.org/10.1016/s0167-8655(99)00065-3.

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Bourbakis, N. G., C. Koutsougeras, and A. Jameel. "Handwritten character recognition using low resolutions." Engineering Applications of Artificial Intelligence 12, no. 2 (April 1999): 139–47. http://dx.doi.org/10.1016/s0952-1976(98)00062-1.

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El-Sheikh, T. S., and S. G. El-Taweel. "Real-time arabic handwritten character recognition." Pattern Recognition 23, no. 12 (January 1990): 1323–32. http://dx.doi.org/10.1016/0031-3203(90)90078-y.

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., Nikita Sarkar. "HANDWRITTEN CHARACTER RECOGNITION USING METHOD FILTERS." International Journal of Research in Engineering and Technology 03, no. 11 (November 25, 2014): 140–43. http://dx.doi.org/10.15623/ijret.2014.0311021.

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Yadav, Madhuri, Ravindra Kumar Purwar, and Mamta Mittal. "Handwritten Hindi character recognition: a review." IET Image Processing 12, no. 11 (November 1, 2018): 1919–33. http://dx.doi.org/10.1049/iet-ipr.2017.0184.

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Ning, Zihao. "Research on Handwritten Chinese Character Recognition Based on BP Neural Network." Modern Electronic Technology 6, no. 1 (June 23, 2022): 12. http://dx.doi.org/10.26549/met.v6i1.11359.

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The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network, and then verify the accuracy and feasibility of the neural network through GUI (Graphical User Interface) model established by Matlab. This paper mainly includes the following aspects: Firstly, the preprocessing process of handwritten Chinese character recognition in this paper is analyzed. Among them, image preprocessing mainly includes six processes: graying, binarization, smoothing and denoising, character segmentation, histogram equalization and normalization. Secondly, through the comparative selection of feature extraction methods for handwritten Chinese characters, and through the comparative analysis of the results of three different feature extraction methods, the most suitable feature extraction method for this paper is found. Finally, it is the application of BP neural network in handwritten Chinese character recognition. The establishment, training process and parameter selection of BP neural network are described in detail. The simulation software platform chosen in this paper is Matlab, and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition. Design the GUI interface of human-computer interaction based on Matlab, show the process and results of handwritten Chinese character recognition, and analyze the experimental results.

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

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

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Haithem Abd Al-RaheemTaha. "ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM." Diyala Journal of Engineering Sciences 5, no. 1 (June 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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Alharbi, Abir. "A Genetic-LVQ neural networks approach for handwritten Arabic character recognition." Artificial Intelligence Research 7, no. 2 (November 26, 2018): 43. http://dx.doi.org/10.5430/air.v7n2p43.

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

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

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

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

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

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

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

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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 (August 30, 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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Rathore, Priti Singh, and Dr Pawan Kumar. "Hand-written Character Recognition with Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1709–15. http://dx.doi.org/10.22214/ijraset.2022.40951.

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Abstract: Various handwriting styles are unique in this manner, making it challenging to identify characters that were written by hand. Handwritten character recognition has become the subject of exploration over the last few decades through an exploration of neural networks. Languages written from left-to-right, such as Hindi, are read from start-to-finish design. To recognize these types of writing, we present a Deep Learning-based handwritten Hindi character recognition system utilizing deep learning techniques such as Convolutional Neural Networks (CNN) with Optimizer Adaptive Moment Estimation (Adam) and Deep Neural Networks (DNN) in this paper. The suggested system was trained on samples from a large number of database images and then evaluated on images from a user-defined data set, yielding extremely high accuracy rates. Keywords: Deep learning, CNN, Adam Optimizer, Handwritten character recognition, Accuracy, Training Time.

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

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

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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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Attigeri, Savitha. "Neural Network based Handwritten Character Recognition system." International Journal Of Engineering And Computer Science 7, no. 03 (March 22, 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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Sureshkumar, C., and T. Ravichandran. "Handwritten Tamil Character Recognition Using RCS Algorithm." International Journal of Computer Applications 8, no. 8 (October 10, 2010): 21–25. http://dx.doi.org/10.5120/1228-1787.

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C.Bharathi, V., and M. Kalaiselvi Geetha. "Segregated Handwritten Character Recognition using GLCM features." International Journal of Computer Applications 84, no. 2 (December 18, 2013): 1–7. http://dx.doi.org/10.5120/14545-2644.

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SAYSOURINHONG, Latsamy, Bilan ZHU, and Masaki NAKAGAWA. "Online Handwritten Lao Character Recognition by MRF." IEICE Transactions on Information and Systems E95.D, no. 6 (2012): 1603–9. http://dx.doi.org/10.1587/transinf.e95.d.1603.

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Journal articles on the topic 'Handwritten Character Recognition (HCR)'

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