Offline handwritten Chinese character recognition by radical decomposition

This paper proposes an approach combining the set of Krawtchouk moments and HMMs (Hidden Markov Models) for unconstrained offline handwritten Chinese character recognition. The Krawtchouk moments can be effectively used as pattern features in analysis of two-dimensional images. Since the basis set is orthogonal in the discrete domain of the image coordinate space, the implementation of Krawtchouk moments doesn't involve any numerical approximation or coordinate transformation. This property makes Krawtchouk moments superior to conventional, more continuous moments such as Legendre moments and Zernike moments. In the field of character recognition, HMMs architecture has been used widely because of its strong adaptability to written distortion and variability. In this paper, Krawtchouk moments and HMMs are combined to recognize unconstrained offline handwritten Chinese amount in words. Experiments show that the average recognition of Krawtchouk is higher than Zernike and Legendre moments within the HMM framework.

Based on the rough set theory and variable granular theorem, a kind of decision information system of off-line handwritten Chinese character recognition is constructed. The performance index entropy in this decision information system is defined in this paper. According to the definition of attribute relative significance, the feature attributes are classified, that the representation of feature attributes in single layer and single granularity become multiple layers and multiple granularities, and a hierarchical reduction approach is designed. The weak points of the single granularity that fixed and reduction is not fine are made up. Based on the D-S evidence theory, the decision rules after reduction are fused, so that the recognition rate can be improved. The experiment results show that the method is feasible and effective.

In recent years, the transformer model has been widely used in computer-vision tasks and has achieved impressive results. Unfortunately, these transformer-based models have the common drawback of having many parameters and a large memory footprint, causing them to be difficult to deploy on mobiles as lightweight convolutional neural networks. To address these issues, a Vision Transformer (ViT) model, named the lightweight Vision Transformer (LW-ViT) model, is proposed to reduce the complexity of the transformer-based model. The model is applied to offline handwritten Chinese character recognition. The design of the LW-ViT model is inspired by MobileViT. The lightweight ViT model reduces the number of parameters and FLOPs by reducing the number of transformer blocks and the MV2 layer based on the overall framework of the MobileViT model. The number of parameters and FLOPs for the LW-ViT model was 0.48 million and 0.22 G, respectively, and it ultimately achieved a high recognition accuracy of 95.8% on the dataset. Furthermore, compared to the MobileViT model, the number of parameters was reduced by 53.8%, and the FLOPs were reduced by 18.5%. The experimental results show that the LW-ViT model has a low number of parameters, proving the correctness and feasibility of the proposed model.

This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.

Recognition of printed and handwritten text (characters and digits) has been a challenge for many researchers in the field of Document Image Analysis for almost thirty years. Many existing pattern recognition methods have been used and new ones have been proposed. Despite the satisfactory results obtained in recognizing printed text, recognition of handwritten text is still an open research. This paper presents a method for off-line recognition of handwritten Chinese characters. Each character is described by six structural features extracted using the dominant point method. The goodness of each feature is determined by a method called stroke probability. The classification was performed by the Fuzzy ARTMAP neural network within the context of the one-class problem approach. Evaluation of the proposed method was determined using database of 900 characters (an average of 40 samples/character). The average mean recognition rate was approximately 94%.

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. The use of an image-only model means that no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.

Offline handwritten chinese character recognition (HCCR) is one of means for quick text input and it has a great demand in the area of file recognition, form processing, machine translation and office automation. However it still is a difficult task for handwritten chinese character recognition to put into practical use because of its large stroke change, writing anomaly, and no stroke ranking information can get, etc. al. An efficient classifier occupies very important position for increasing offline HCCR ratio. Support vector machines offer a theoretically well-founded approach to automated learning of pattern classifiers for mining labeled data sets. But as we know, the performance of SVMs largely depend on the kernel function, different kernel function will produce different SVMs, and may result in different performance. However, there are no theories concerning how to choose good kernel functions based on practical using problem. In this paper we make use of the basic properties of Mercer kernel to construct a hybrid kernel from the existing common kernel, and to find the unknown parameters of the hybrid kernel in data-dependent way by minimizing the upper bound of the VC dimension of the set of functions. Our experiment results show that the proposed method is efficient compared with other classifier for handwritten Chinese character recognition.

Deep convolutional neural networks (DNNs) have achieved outstanding performance in this field. Meanwhile, handwritten Chinese character recognition (HCCR) is a challenging area of research in the field of computer vision. DNNs require a large number of parameters and high memory consumption. To address these issues, this paper proposes an approach based on an attention mechanism and knowledge distillation. The attention mechanism improves the feature extraction and the knowledge distillation reduces the number of parameters. The experimental results show that ResNet18 achieves a recognition accuracy of 97.63% on the HCCR dataset with 11.25 million parameters. Compared with other methods, this study improves the performance for HCCR.

Modeling heterogeneity in field crops is a key issue for a better characterization of field production. This paper presents some experimental data on sugar beet illustrating this heterogeneity. Several sources of individual variability within plant populations are identified: namely, initial condition (seed biomass, emergence delay), genetic variability (including phyllochron) and environment (including spacing and competition). A mathematical framework is introduced to integrate the different sources of variability in plant growth models. It is based on the classical method of Taylor Series Expansion, which allows the propagation of uncertainty in the dynamic system of growth and the computation of the approximate means and standard deviations of the model outputs. The method is applied to the GreenLab model of plant growth and more specifically to sugar beet. It opens perspectives in order to assess the different sources of variability in plant populations and estimate their parameters from experimental data. However important issues like optimization of data collection and system identifiability have to be resolved first, since the uncertainty effects may be mixed in an inextricable way or may necessitate a too huge amount of experimental data for their estimation.

For manufacturing companies, one of the most important decisions is to be able to offer short, reliable lead times to their customers. Here we examine the topic with the focus on the robustness of the due date setting decisions, which means taking into account the variability in the shop floor, or changes in the planned schedules. We first review the literature on the topic and classify the contributions from three different viewpoints. One outcome of the review is the fact that most approaches assume a single machine setting, and do not investigate the problem of establishing a time buffer in the due date quotation process in order to cope with different sources of variability. Therefore, we carry out an introductory experiment to analyse, in a relatively complex setting, the suitability of different due date setting methods under different sources of variability, and derive some preliminary results.

There is a special induction field in the figures of characters which is a physiological and psychological phenomenon and is highly related to human character recognition. In this paper, a formal definition of the field transform is introduced, in which the field of a character image is simulated by a 2-dimensional scalar field which is caused by all the stroke points in the character image. If can be applied to both binary and gray-level images. A very simple character recognition approach is also proposed based on the field transform and is applied to personal handwritten Chinese character recognition. Preliminary experimental results are very promising. Further efforts should be made to find out new ways of utilizing the field transform for character recognition and other applications.

Most Latin word and character recognition systems have numerous classification algorithms to automate the identification of handwritten characters. However, despite extensive research on various Latin-based languages, relatively limited attention has been given to the recognition of handwritten text in Nigerian indigenous languages. Handwritten character recognition is challenging due to the variations in shape and style. In addition to that, these characters naturally vary among individuals. This work presents an offline character recognition system for Igbo handwritten alphabets using convolutional neural networks. In this study, a dataset comprising 9,000 character images was created, with 250 distinct samples for each of the 36 letters in the Igbo alphabet. Of these, 200 samples per character (a total of 7,200 images) were pre-processed, binarized, and normalized for training two Convolutional Neural Network (CNN) models. The remaining 50 samples per character (1,800 images) were reserved for testing. Model 1 and Model 2 achieved recognition accuracies of 83.5% and 85.0%, respectively. The proposed approach demonstrates potential for enhancing the automation and digitization of handwritten Igbo texts into machine-readable formats. Keywords: Optical Character Recognition, Igbo Handwritten Characters, Nigerian, Indigenous, Convolutional Neural Network Journal Reference Format: Woods, N.C. & Oyedeji, O.A. (2025): Offline Handwritten Character Recognition for Igbo Alphabets Using Convolutional Neural Networks. Journal of Behavioural Informatics, Digital Humanities and Development Rese Vol. 11 No. 2. Pp 115-126 https://www.isteams.net/behavioralinformaticsjournal dx.doi.org/10.22624/AIMS/BHI/V11N2P11

Parameterization of wave runup is of paramount importance for an assessment of coastal hazards. Parametric models employ wave (e.g., Hs and Lp) and beach (i.e., β) parameters to estimate extreme runup (e.g., R2%). Thus, recent studies have been devoted to improving such parameterizations by including additional information regarding wave forcing or beach morphology features. However, the effects of intra-wave dynamics, related to the random nature of the wave transformation process, on runup statistics have not been incorporated. This work employs a phase- and depth- resolving model, based on the Reynolds-averaged Navier-Stokes equations, to investigate different sources of variability associated with runup on planar beaches. The numerical model is validated with laboratory runup data. Subsequently, the role of both aleatory uncertainty and other known sources of runup variability (i.e., frequency spreading and bed roughness) is investigated. Model results show that aleatory uncertainty can be more important than the contributions from other sources of variability such as the bed roughness and frequency spreading. Ensemble results are employed to develop a new parametric model which uses the Hunt (J Waterw Port Coastal Ocean Eng 85:123–152, 1959) scaling parameter $\beta \left (H_{s}L_{p}\right )^{1/2}$.

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.