Handwritten Character Recognition Research Articles

Automatic arabic handwritten characters Recognition using ensemble of convolutional neural networks from scratch

Automatic arabic handwritten characters Recognition using ensemble of convolutional neural networks from scratch

Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network

Handwritten Character recognition falls under the domain of image classification that has been under research for years. The idea is to make the machine recognize handwritten human characters. The language focused in this research paper is English while using offline handwritten character recognition for identifying English characters. There are many publically available datasets from which EMNIST is the most challenging one. The main idea of this research paper is to propose a deep learning CNN method to help recognize English characters. This research paper proposes a deep learning convolutional neural network that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyper-parametric settings were used for all the models under test and E-Character with the same data augmentation settings. The proposed model named the E-Character recognizer was able to produce 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some of the problems like misclassification due to the similar structure of characters.

A review of neural networks in handwritten character recognition

Handwritten character recognition has been a significant research focus in the fields of pattern recognition and artificial intelligence over the past decade. With the advent of neural networks, particularly deep learning models, the accuracy and efficiency of offline handwritten character recognition have dramatically improved. This paper presents a comprehensive review of recent developments in applying neural networks to handwritten character recognition. The literature review covers studies conducted between 2016 and 2023, providing insights into the methodologies, data processing techniques, and evaluation metrics used. The review spans various neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. It categorizes and compares their performance across multiple benchmark datasets, highlighting specific improvements in recognition accuracy and efficiency. Furthermore, the review discusses the challenges faced in large-scale datasets, such as the diversity of handwriting styles and computational cost constraints. Notably, CNNs have shown outstanding performance, but the integration of advanced techniques like transfer learning and Generative Adversarial Networks (GANs) is explored as a potential avenue to enhance future recognition systems.

Optical activation function using a metamaterial waveguide for an all-optical neural network.

In this study, we experimentally demonstrated that the nonlinear optical coefficient of the original Si can be enhanced by incorporating a metamaterial structure into an existing silicon waveguide. The two-photon absorption coefficient enhanced by the metamaterial structure was 424 cm/GW, which is 1.2 × 103 times higher than that of Si. Using this metamaterial waveguide-based nonlinear optical activation function, we achieved a high inference accuracy of 98.36% in the handwritten character recognition task, comparable to that obtained with the ReLU function as the activation function. Therefore, our approach can contribute to the realization of more power-efficient and compact all-optical neural networks.

Fine-grained Automatic Augmentation for handwritten character recognition

With the advancement of deep learning-based character recognition models, the training data size has become a crucial factor in improving the performance of handwritten text recognition. For languages with low-resource handwriting samples, data augmentation methods can effectively scale up the data size and improve the performance of handwriting recognition models. However, existing data augmentation methods for handwritten text face two limitations: (1) Methods based on global spatial transformations typically augment the training data by transforming each word sample as a whole but ignore the potential to generate fine-grained transformation from local word areas, limiting the diversity of the generated samples; (2) It is challenging to adaptively choose a reasonable augmentation parameter when applying these methods to different language datasets. To address these issues, this paper proposes Fine-grained Automatic Augmentation (FgAA) for handwritten character recognition. Specifically, FgAA views each word sample as composed of multiple strokes and achieves data augmentation by performing fine-grained transformations on the strokes. Each word is automatically segmented into various strokes, and each stroke is fitted with a Bézier curve. On such a basis, we define the augmentation policy related to the fine-grained transformation and use Bayesian optimization to select the optimal augmentation policy automatically, thereby achieving the automatic augmentation of handwriting samples. Experiments on five handwriting datasets of different languages demonstrate that FgAA achieves the best augmentation effect for handwritten character recognition. Our code is available at https://github.com/IMU-MachineLearningSXD/Fine-grained-Automatic-Augmentation

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology, from two perspectives: traditional recognition methods and deep learning-based recognition methods. Firstly, the complexity of Chinese character recognition is pointed out, including its numerous categories, complex structure, and the problem of similar characters, especially the variability of handwritten Chinese characters. Subsequently, recognition methods based on feature optimization, model optimization, and fusion techniques are highlighted. The fusion studies between feature optimization and model improvement are further explored, and these studies further enhance the recognition effect through complementary advantages. Finally, the article summarizes the current challenges of Chinese character recognition technology, including accuracy improvement, model complexity, and real-time problems, and looks forward to future research directions.

Deep Learning Function Implications For Handwritten Character Recognition Using Improved Mean Square Error Function

تستخدم دوال الخسارة لتقييم دقة أداء نموذج التصنيف في تعلم الماكنة. قيمة معدل الخسارة يقيس اذا ما كان قيم التسميات المتوقعة مطابقة الى التسميات الحقيقة او قريبة منها. تعد تمييزالحروف المكتوبة بخط اليد من مشاكل تمييز الانماط. استخدام التعلم العميق سرع التطور الحاصل في تطبيقات تمييز الانماط لتصنيف الصورالمخزونة.تعد الشبكات العصبية التلافيفية ودالة التفعيل الاكثر استخداما في التعلم العميق. اضافة الى استخدام دوال خسارة مختلفة لتقييم أداء النموذج مثل Categorical Cross-Entropy (CCE) و Mean Square Error (MSE). في تقنيات التعلم العميق, حجم قاعدة البيانات يلعب دور مهم للحصول على أداء عالي, ولكن مع MSE التقليدية قيم الخسارة تصل الى الصفر في مراحل مبكرة من عملية التدريب مع قاعدة البيانات الكبيرة الحجم, بالرغم من كون دقة النموذج ما زالت تتحسن. هذه الدراسة تقترح دالة خسارة مطورة عن طريق تحسين MSE. تستند MSE المقترحة على تقسيم مربع الخطأ على مجموع احتمالات التسمية المتوقعة بدل من مجموع عدد العينات. تم استخدام خمسة قواعد بيانات لاختبار اداء النموذج المقترح الشبكات العصبية التلافيفية مع MSE المطوره بالاضافة الى استخدام نموذج VGG16 المعدل. وقواعد البيانات هم AHCD, AIA9K, HIJJA, Self-collected و MNIST. اظهرت نتائج الدراسة تطور واضح في دقة اداء النموذج المقترح مع متوسط مربع الخطأ MSE المطورة مقارنة ب CCE.

Handwritten Character Recognition using Enhanced Artificial Neural Network

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.

Evaluating Handwritten Character Recognition with Hu Moments and K-Nearest Neighbors Algorithm

Handwritten character recognition remains a challenging task in various domains, including handwriting analysis and document digitization. This task poses a significant challenge because of the variability and complexity of handwriting. To address this, it is crucial to develop different methodologies that enhance accuracy and efficiency in recognition. This research evaluates the discrimination capacity of a classifier that combines together the Hu Moments features extraction technique and the k-nearest neighbors (KNN) algorithm for the purpose of handwritten character recognition. The authors worked with handwritten characters from the English alphabet where each character is represented in two handwritten forms. The average accuracy of recognition was 82.69%, that conforms the efficiency of the proposed classifier. This research contributes to the field of automated handwritten recognition and highlights the necessity for improved techniques in character analysis, which has implications for handwriting recognition in different contexts, linguistic studies, document digitization, and other applications where character recognition is crucial. This is the first study that aims to apply the combination of Hu Moments features extraction technique in combination with the KNN algorithm for purpose of handwritten character recognition.

Enhancing Asian Indigenous Language Processing through Deep Learning-based Handwriting Recognition and Optimization Techniques

Asian indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages have attained important internet amongst the research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low-resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is the most ancient Indian language that is mostly exploited in the Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning-based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in the Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs a capsule network (CapsNet) model for feature vector generation with the HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.

SVM, KNN, and Neural Networks Investigated for Machine Learning in Written Word Decoding

The capacity of a device to recognise and understand legible handwriting input from a variety of origins, including written material, snap shots, displays, and other electronics, is known as handwritten reputation. In this study, we investigate three classification algorithms: Support Vector Machines (SVM), K-Nearest_Neighbours (KNN), and Neural Networks for handwritten character recognition, and we will identify the best one among these three.

A novel methodology for offline English handwritten character recognition using ELBP-based sequential (CNN)

Handwritten character recognition falls under the domain of image classification, which has been under research for years. But still, specific gaps need to be highlighted as offline handwritten character recognition (OHCR) with the limitation of the unstructured hierarchy of character classification. However, the idea is to make the machine recognize handwritten human characters. The language focused on in this research paper is English, using offline handwritten character recognition for identifying English characters. There are many publicly available datasets, of which EMNIST is the most challenging. The key idea of this research paper is to recommend a deep learning-based ELBP-CNN method to help recognize English characters. This research paper proposes a deep learning CovNet with feature extraction and novel local binary pattern-based approaches, LBP (AND, OR), that is tested and compared with renowned pre-trained models using transfer learning. These parametric settings address multiple issues and are finalized after experimentation. The same hyperparametric settings were used for all the models under test and E-Character, with the same data augmentation settings. The proposed model, named the E-Character recognizer, produced 87.31% accuracy. It was better than most of the tested pre-trained models and other proposed methods by other researchers. This research paper further highlighted some problems, like misclassification due to the similar structure of characters.

Deep convolutional neural network for Lampung character recognition

Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.

Increasing Offline Handwritten Chinese Character Recognition Using Separated Pre-Training Models: A Computer Vision Approach

Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers’ styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision.

Feature Extraction Algorithm based Metaheuristic Optimization for Handwritten Character Recognition

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.

A Comprehensive Approach for Tamil Handwritten Character Recognition with Feature Selection and Ensemble Learning

A Comprehensive Approach for Tamil Handwritten Character Recognition with Feature Selection and Ensemble Learning

VashaNet: An automated system for recognizing handwritten Bangla basic characters using deep convolutional neural network

Automated character recognition is currently highly popular due to its wide range of applications. Bengali handwritten character recognition (BHCR) is an extremely difficult issue because of the nature of the script. Very few handwritten character recognition (HCR) models are capable of accurately classifying all different sorts of Bangla characters. Recently, image recognition, video analytics, and natural language processing have all found great success using convolutional neural network (CNN) due to its ability to extract and classify features in novel ways. In this paper, we introduce a VashaNet model for recognizing Bangla handwritten basic characters. The suggested VashaNet model employs a 26-layer deep convolutional neural network (DCNN) architecture consisting of nine convolutional layers, six max pooling layers, two dropout layers, five batch normalization layers, one flattening layer, two dense layers, and one output layer. The experiment was performed over 2 datasets consisting of a primary dataset of 5750 images, CMATERdb 3.1.2 for the purpose of training and evaluating the model. The suggested character recognition model worked very well, with test accuracy rates of 94.60% for the primary dataset, 94.43% for CMATERdb 3.1.2 dataset. These remarkable outcomes demonstrate that the proposed VashaNet outperforms other existing methods and offers improved suitability in different character recognition tasks. The proposed approach is a viable candidate for the high efficient practical automatic BHCR system. The proposed approach is a more powerful candidate for the development of an automatic BHCR system for use in practical settings.

Bangla Handwritten Character Recognition Using Deep Learning Approaches and its Explainability With AI

Bangla Handwritten Character Recognition Using Deep Learning Approaches and its Explainability With AI

ML & DL approach for Handwritten Character Recognition for Gujarati Characters

ML & DL approach for Handwritten Character Recognition for Gujarati Characters

Gujarati handwritten character and modifiers recognition using deep hybrid classifier

Gujarati handwritten character and modifiers recognition using deep hybrid classifier