Handwritten Character Recognition System Research Articles

Handwritten Text Recognition

Handwritten text detection refers to the capacity of a computer system to interpret and understand handwritten input from various sources such as paper documents, touch displays, and photographs. Within the realm of pattern recognition, one specific area is handwritten text recognition, which involves categorizing and interpreting handwritten text. In this study, we propose a novel approach to offline handwritten text recognition utilizing deep neural networks. The availability of vast amounts of data and continuous algorithmic enhancements have made training neural networks more accessible than ever before. This handwritten character recognition system relies on image segmentation to accurately identify and interpret handwritten characters. Through this approach, we aim to improve the accuracy and efficiency of recognizing handwritten text across various applications and contexts.

OHSCR: Benchmarks Dataset for Offline Handwritten Sindhi Character Recognition

This research work presents a unique dataset for offline handwritten Sindhi character recognition. It has 7800 character images in total, divided into multiple categories by 150 writers of various ages, genders, and professional backgrounds. Each writer writes the 52 Sindhi characters in the designed form. With a high-quality scanner, all of the written samples were scanned. After that, all the handwritten Sindhi characters were cropped from the collected designed form, and the cropped images were saved in ‘.png’ format. For the benefit of the Sindhi research community, this work suggests an image dataset for character recognition in handwritten Sindhi. The dataset will be made publically available. For the Sindhi language, this dataset can be used to create and test handwritten character recognition systems and provide helpful insights through writer identification. The dataset has been divided into the training set and the test set, with 80% for training and 20% for testing. The different preprocessing techniques used to remove noise from scanned images to create a clean dataset. The dataset created as a result of this research is the world's first openly accessible dataset for handwritten research, and it can be useful for writer identification systems and handwriting recognition systems.

Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition

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 analysed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy.

BNVGLENET: Hypercomplex Bangla handwriting character recognition with hierarchical class expansion using Convolutional Neural Networks

Object recognition technology has made significant strides where recognizing handwritten Bangla characters including symbols, compounds form, etc. remains a challenging problem due to the prevalence of cursive writing and many ambiguous characters. The complexity and variability of the Bangla script, and individual’s unique handwriting styles make it difficult to achieve satisfactory performance for practical applications, and the best existing recognizers are far less effective than those developed for English alpha-numeric characters. In comparison to other major languages, there are limited options for recognizing handwritten Bangla characters. This study has the potential to improve the accuracy and effectiveness of handwriting recognition systems for the Bengali language, which is spoken by over 200 million people worldwide. This paper aims to investigate the application of Convolutional Neural Networks (CNNs) for recognizing Bangla handwritten characters, with a particular focus on enlarging the recognized character classes. To achieve this, a novel challenging dataset for handwriting recognition is introduced, which is collected from numerous students’ handwriting from two institutions. A novel convolutional neural network-based approach called BNVGLENET is proposed in this paper to recognize Bangla handwritten characters by modifying the LeNet-5 and combining it with the VGG architecture, which has the advantage of significantly identifying the characters from Bengali handwriting. This study systematically evaluated the performance of models not only on custom novel dataset but also on the publicly available Bangla handwritten character dataset called the Grapheme dataset. This research achieved a state-of-the-art recognition accuracy of 98.2% on our custom testing vowel-consonant class and 97.5% on the custom individual class. The improvements achieved in this study bridge a notable disparity between the practical needs and the actual performance of Bangla handwritten character recognition systems.

Enhancing the Odia Handwritten Character and Numeral Recognition System's Performance with an Ensemble of Deep Neural Networks

Enhancing the Odia Handwritten Character and Numeral Recognition System's Performance with an Ensemble of Deep Neural Networks

Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages

Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.

Comprehensive robustness evaluation of an automatic writer identification system using convolutional neural networks

<p>This research paper presents a convolutional neural network (CNN) model for identifying handwritten Urdu characters. A dataset of 38 fundamental Urdu characters from 100 different writers in the Kashmir valley was manually collected. The developed system was trained on a training dataset of 30,400 samples and verified on a test dataset of 7600 samples, and it outperformed previously proposed AI based writer identification systems in Urdu language with an identification rate of 91.44 percent for 38 classes. This study highlights the effectiveness of deep learning techniques in solving the challenging task of the Urdu writer identification. The findings demonstrate the potential of the developed CNN model for real-world applications in handwritten character recognition and verification systems. Future work involves expanding the dataset to include numerals and isolated characters for improved system performance.</p>

Modeling of Hybrid CNN Framework for Handwritten Character and Digit Recognition System

Modeling of Hybrid CNN Framework for Handwritten Character and Digit Recognition System

DEEP CONVOLUTIONAL NEURAL NETWORK USING A NEW DATASET FOR BERBER LANGUAGE

Currently, Handwritten Character Recognition (HCR) technology has become an interesting and immensely useful technology. It has been explored with highperformance in many languages. However, a few HCR systems are proposed for the Amazigh (Berber) language. Furthermore, the validation of any Amazighhandwritten recognition system remains a major challenge due to no availability of a robust Amazigh database. To address this problem, we first created two new datasets for Tifinagh and Amazigh Latin characters, by extending the well-known EMNIST database with the Amazigh alphabet. And then, we have proposed a handwritten character recognition system, which is based on a deep convolutional neural network to validate the created datasets. The proposed CNN has been trained and tested on our created datasets, and the experimental tests show that it achieves satisfactory results in terms of accuracy and recognition efficiency.

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN.

Traditional human-computer interaction technology relies heavily on input devices such as mice and keyboards, which limit the speed and naturalness of interaction and can no longer meet the more advanced interaction needs of users. With the development of computer vision (CV) technology, research on contactless gesture recognition has become a new research hotspot. However, current CV-based gesture recognition technology has the limitation of a limited number of gesture recognition and cannot achieve fast and accurate text input operations. To solve this problem, this paper proposes an over-the-air handwritten character recognition system based on the coordinate correction YOLOv5 algorithm and a lightweight convolutional neural network (LGR-CNN), referred to as Air-GR. Unlike the direct recognition of captured gesture pictures, the system uses the trajectory points of gesture actions to generate images for gesture recognition. Firstly, by combining YOLOv5 with the gesture coordinate correction algorithm proposed in this paper, the system can effectively improve gesture detection accuracy. Secondly, considering that the captured gesture coordinates may contain multiple gestures, this paper proposes a time-window-based algorithm for segmenting the gesture coordinates. Finally, the system recognizes user gestures by plotting the segmented gesture coordinates in a two-dimensional coordinate system and feeding them into the constructed lightweight convolutional neural network, LGR-CNN. For the gesture trajectory image classification task, the accuracy of LGR-CNN is 13.2%, 12.2%, and 4.5% higher than that of the mainstream networks VGG16, ResNet, and GoogLeNet, respectively. The experimental results show that Air-GR can quickly and effectively recognize any combination of 26 English letters and numbers, and its recognition accuracy reaches 95.24%.

DMHC: Device-free multi-modal handwritten character recognition system with acoustic signal

Efficient typing in a mobile device is a long-standing problem because of its small-size touch screen. Methods for contactless solutions using acoustic sensors have been proposed recently, which are device-free and low cost. However, these methods are not practical enough as they use either the microphone of the mobile device for recording the sounds generated during writing or the speaker of the device for playing a frequency modulated ultrasonic signal and then the signal reflected from the hand of the user is analyzed, where the former process requires a quiet environment and the latter process requires the user to write a letter in a single stroke and large size. In order to overcome those challenges, we present DMHC, which is an effective and device-free multi-modal handwritten character recognition system. DMHC fuses two different acoustic modalities, which are ultrasonic and general audio signals. The main feature of DMHC is that we extract the latent interactions between the ultrasonic and general acoustic signals. Specifically, DMHC implements the noise-resistant handwritten character recognition system for reliably recognizing handwritten characters in real life. We implement our proposed module based on the self-attention method to multiple channel fusion. Extensive experimental results demonstrate the effectiveness and robustness of DMHC under various conditions, where the recognition accuracies of letters and words are found to be 97.4% and 95.3%, respectively.

Gujarati Script Recognition

Character recognition is the extraction of printed or handwritten text from images into machine-readable format. The extracted text can be easily edited, modified and efficiently stored. While there are several Optical Character Recognition (OCR) and Handwritten Character Recognition (HCR) systems available for the English language, such systems are not well developed for Indian languages such as Gujarati. This work deals with text recognition of the Gujarati Script. Two different models have been analyzed in this work for the task of recognition of Gujarati text: CNN based EfficientNet B3 and YOLO v4. The system has been developed using the EfficientNet B3 model which gives better accuracy and efficiency. The input to the system is an image having optical Gujarati text and the system produces an editable text document having the contents of the recognized text in the image. The system has been successfully implemented for the task of creating a digital library of Gujarati newspapers articles from their images. This novel project is a step toward the cultural and linguistic preservation of the Gujarati language.

Nepali Handwritten Character Recognition System

Nepali Handwritten Character Recognition System

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available. The widespread use of devices such as smartphones, smartwatches, and tablets has not been enough to convince the user to use pen-based interfaces. This implies that more research on the pen interface and recognition methods is still necessary. This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). First, a user wearing an MYO armband on the forearm writes a multi-stroke freestyle character on a touchpad by using the finger or a pen. Next, the 3D accelerometer signals generated during the writing process are fed into a CNN, LSTM, or CNN-LSTM network for recognition. The convolutional backbone obtains spatial features in order to feed an LSTM that extracts short-term temporal information. The system was evaluated on a proprietary dataset of 3D accelerometer data collected from multiple users with an armband device, corresponding to handwritten English lowercase letters (a–z) and digits (0–9) in a freestyle. The results show that the proposed system overcomes other systems from the state of the art by 0.53%.

OCR for Arabic character recognition with advanced principal component analysis based on feature extraction and fuzzy-KNN based classification

Offline character recognition has become a highly important study field for various pattern recognition applications in recent years. Several handwritten character recognition systems have been suggested, with the complexity of these systems varying depending on the recognizing units' writing styles. In reality, identifying letters or numerals is far simpler than recognizing cursive sentences or lines of text. As a result, early handwriting recognition algorithms could only distinguish a few characters with limited vocabularies. Nowadays Arabic handwritten character recognition is very important as it is very difficult to identify. The cursive writing and variety of styles make this recognition more complex. This paper presents an automated model for ACR. This ACR is constructed from four phases: Preprocessing, Segmentation, Feature Extraction, and Classification. In this research article, we compare the advanced EPSO EKNN Algorithm with the earlier DBN Algorithm and also suggest a new method having higher Accuracy. In this research article, the "Enhanced K-Nearest Neighbor" (EKNN) classification model was used to identify and classify or simply recognize the particular Arabic character, and the "Extended Particle Swarm Optimization" (EPSO) methodology was introduced to select the best feature from feature extraction to comply with Arabic-Character Classification.

A Hybrid System for Handwritten Character Recognition with High Robustness

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

Hand-written Character Recognition with Neural Network

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.

Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches.

Handwritten English Alphabet Recognition

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.

Optical Recognition of Handwritten Logic Formulas Using Neural Networks

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.