Performance Evaluation of Different Algorithms for Handwritten Isolated Bangla Character Recognition

Bangla handwritten character recognition is a popular research topic as its difficulty is higher than the recognition of other languages because of multiple formats of compound characters. State of the art Convolutional neural network (CNN) architectures are very much useful in computer vision applications. Some works have been carried out in Bangla handwritten character recognition but most of them either not very efficient or they can not classify a lot of characters. In this work, state of art pre-trained CNN architectures is used to classify 231 different Bangla handwritten characters using CMATERdb dataset. The images were first converted to B&W form with white as the foreground color. The size of the images is reduced to 28 × 28 form. These images are used as input to the CNN architectures. The weights of the state-of-the-art CNN models are kept as it was. The training learning rate was set to 0.001 and categorical cross-entropy as the error function. After 50 epochs, InceptionResNetV2 achieved the best accuracy (96.99%). DenseNet121 and InceptionNetV3 also provided remarkable recognition accuracy (96.55 and 96.20%, respectively). We also considered combination of trained InceptionResNetV2, InceptionNetV3 and DenseNet121 architectures which provided better recognition accuracy (97.69%) than other single CNN architectures but it is not feasible for using as it requires a lot of computation power and memory. The models were tested in the cases where characters look confusing to humans, but all the architectures showed equal capability in recognizing these images. Considering computational complexity, memory and capability of recognizing confused characters, InceptionResNetV2 can be said as the best performing model.

The present work deals with recognition of handwritten characters of Bangla, a major script of the Indian sub-continent. The main contributions presented here are (a) generation of a database of handwritten basic characters of Bangla and (b) development of a handwritten character recognition scheme suitable for scripts like Bangla consisting of many similar shaped characters for the benchmark results. The present database is a pioneering development in the context of recognition of off-line handwritten characters of this script. It has 37,858 handwritten samples and accommodates a large spectrum of handwriting style by Bangla speaking population. This database will be made available ( http://www.isical.ac.in/~ujjwal/download/Banglabasiccharacter.html ) free of cost to researchers for further studies. Also, we identified two major factors affecting high recognition accuracies for the present character samples, namely, (a) erratic nature of the presence of headline (shapes of Bangla characters usually contain a horizontal line in its upper part) and (b) existence of several pairs of similar shaped characters. The proposed recognition approach takes care of the above factors. It identifies any confusion in the first stage classification between a pair of similar shaped character classes and resolves the same in the second stage classification by extracting a feature vector based on a non-uniform grid.

Image classification is one of the fundamental tasks in the field of machine learning. This study focuses on Bangla handwritten character recognition (BHCR). Recognizing handwritten characters has numerous applications like optical character recognition, post office automation, signboard recognition, number plate recognition, etc. A system that can classify Bangla handwriting on the character level efficiently on a large scale would surely be a primary step to achieve those autonomous user experiences. This study has developed a stacked generalization ensemble system that consists of six convolutional neural networks (CNN). The ensembled CNN model of this study has been able to perform more accurately than the individual CNN models. It can recognize 122 different Bangla handwritten characters with a test accuracy of 96.72% and an f1-score of 96.70%. The system has also outperformed most of the existing works by achieving better performance and by recognizing more handwritten characters.

A hierarchical approach to recognition of handwritten Bangla characters

Handwritten character recognition has various potential in the field of document image processing. It is one of the important aspects for systems like handwritten optical character recognizer, writer identification/verification, automatic document sorter etc. In Bangla only few attempts are made towards character recognition. In this current study a relatively new attempt is made towards finding the dependency of writer information on character recognition by varying the inputs. This study will provide a better understanding of the input data for character recognition. Also it will help to know the Bangla characters better for writer identification/verification. Here, highest accuracy of 100% is achieved in case of numeral 7 applying LibSVM classifier.

Automating handwritten character recognition (HCR) for Sinhalese is still an emerging field, as Sri Lanka is the only country in the world that uses Sinhala as the national language. The Sinhala alphabet includes 60 characters that appear to be fairly complex when compared to many other languages. Although approximately 25 research studies have been conducted since 1990s to recognize handwritten Sinhala characters, there is still a large room for improvement. We have investigated two different methods for handwritten Sinhala character recognition: (i) total pixel length-based method which was newly derived by assuming the differences among characters in pixel length and (ii) Convolution Neural Networks (CNN) based classification, to overcome the limitations in the primitive methods. Multiple preprocessing techniques were used in the pixel length-based method while only a resizing technique was used in the CNN based method. The total pixel length and CNN based methods were tested using a dataset of 55 characters, each with 33 samples. The result of the pixel length method is imperfect as distinct characters may hold the same length. Therefore, we abode this method for further processing in future with more improvements. But the proposed CNN classification produces an accuracy of 85.71% and when compared to current studies, this can be considered as the best result achieved for 55 characters among total 60 characters, with nearly 10% of accuracy improvement.

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

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

Handwritten Chinese character recognition (HCCR) is an important research field of pattern recognition, which has attracted extensive studies during the past decades. Recently convolutional neural network (CNN) based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition. Nevertheless, handwritten Chinese character recognition is still limited to be effectively used in the actual environment due to the large-scale vocabulary and great diversity of handwriting style. In this paper, we constructed a handwritten Chinese character recognition service based on convolutional neural network, which tries to make effective use of handwritten based printed fonts and existing handwritten database. At the same time, the service can effectively collect more handwritten data to expand the training dataset, which makes it easy to adapt to the new handwriting styles. Meanwhile, We propose a multi-level recognition theory applied to online handwritten Chinese character recognition, which may improve the accuracy of handwritten Chinese character recognition and break the limitations of handwritten Chinese character recognition by identifying the structure of Chinese characters and possible stroke orders firstly. Furthermore, we try to apply the method of online character recognition to the offline character recognition based on the basic writing rules.

The deep learning networks has become an active area in the realm of machine learning in recent years and some relevant models such as convolutional neural network (CNN) models have a good performance in handwritten character recognition. The MNIST is a typical open source data set, which only has ten kinds of handwritten digital characters from 0 to 9. Furthermore, the majority of the open source data sets in the current literature exclude arithmetic operators of "+" , "-" , "×" , "÷" , "=". In order to address this issue, firstly, we established a data sets of 22,200 characters, which includes fifteen categories. Specially, for each category, 1,200 characters is used for training and 280 characters is used for testing respectively. Secondly, we proposed a deep learning networks algorithm for characters recognition based on CNN. Thirdly, as an application, we further implemented four arithmetic operation using the recognized characters. The experimental results demonstrate that the proposed deep learning networks algorithm has shown strong ability for feature extraction and classification with the average accuracy of 97.42 %. More meaningfully, the four arithmetic operation in the present work has strong robustness and the potential applied value.

With the wide application of artificial intelligence technology in intelligent education, intelligent office and other fields, handwritten optical character recognition (OCR) as an important content of intelligent education research has been widely concerned. Traditional character recognition technology is mainly for high-quality printed images, but there are some problems in English handwriting, such as irregular handwriting, overlapping letters, word smearing, etc., which can not achieve the recognition accuracy of printed images. In particular, the lack of database construction for handwriting images with scrawl and daub increases the difficulty of model training, so handwriting recognition is a very challenging task. In view of the existing problems mentioned above and the limitations of traditional handwriting recognition methods, this paper studies the English handwriting recognition method based on deep learning technology, and realizes the English handwriting image detection and recognition system on this basis. The main tasks of this paper are as follows: 1. The database of English handwriting image is established, including neat, scrawl, neat and smeared and scrawl and smeared. Label information is assigned to each type of data, and SVM classifier is trained to classify new samples using this database. In order to reduce the interference of smear area and background information on the character recognition model, the image is preprocessed with smear block removal, gray scale and tilt correction, and the processing results are used as the input of the subsequent detection and recognition model. 2. A convolutional cyclic neural network based on cross-channel and cross-space attention mechanism is proposed. The convolutional neural network is used to extract local feature information and cross-channel and cross-space attention mechanisms are introduced to reduce the loss of key features. At the same time, bidirectional recurrent neural network is used to capture the context dependence of characters and obtain the word order information of English script. 3. An English handwritten recognition method based on Semantic Reasoning Networks (SRN) and CCSACRNN (SNCN) is proposed.

Handwritten character recognition (HCR) is the detection of characters from images, documents and other sources and changes them in machine-readable shape for further processing. The accurate recognition of intricate-shaped compound handwritten characters is still a great challenge. Recent advances in convolutional neural network (CNN) have made great progress in HCR by learning discriminatory characteristics from large amounts of raw data. In this paper, CNN is implemented to recognize the characters from a test dataset. The main focus of this work is to investigate CNN capability to recognize the characters from the image dataset and the accuracy of recognition with training and testing. CNN recognizes the characters by considering the forms and contrasting the features that differentiate among characters. Our CNN implementation is experimented with the dataset NIST to obtain the accuracy of handwritten characters. Test result provides that an accuracy of 92.91% accuracy is obtained on 200 images with a training set of 1000 images from NIST.

Handwritten character recognition (HCR) is the most challenging task because it is a repeated work which is done by humans, and because of that, error can occur. We can solve this problem with image classification and deep learning algorithms, but in any language, because of similar type of character and different type of writing styles, it is the most challenging task. There are many single language character recognition models available, but there is no model available for multiple language character recognition. When number of classes increases, model performance will decrease. There are many techniques available for HCR. For this problem, Convolutional Neural Network (CNN) is used widely because it is a state-of-the-art model for image classification. In this, a new architecture is proposed to recognize any character, independent of language. The proposed architecture was fine-tuned by changing the hyper-parameters and choosing the appropriate activation function. The proposed system was evaluated on three different publicly available datasets, that includes English, Hindi, Bengali characters, and also on mathematical symbols. We have achieved an accuracy of 99.54%, 98.60%, 87.10%, and 95.77, respectively, for uni-lingual models. For the proposed multi-lingual model, we have achieved an accuracy of 95.73%. By comparative analysis with the existing methods, it is shown that there is a significant improvement in the performance of the proposed multi-lingual model.

It is highly desirable to develop an offline handwritten character recognition system for the Hindi script with an acceptable accuracy level. This script has many characters, including numerals and different shape variations of these characters. Moreover, the Hindi script also has compound characters as well as characters with modifiers. It makes the task of character recognition more complex. Even the shape of characters written by an individual may have significant variation at different times. Hence, incorporating all these issues is very challenging. This paper proposes the augmented data set to overcome these problems up to an extent. The augmented data set introduces the hypothetical changes in the original data set to increase the variations in the data samples, which may increase the variety of samples. Thus, the accuracy of the recognition system improves. For the classification of characters, Convolutional Neural Networks (CNNs) are employed. Based on CNN, classification models are more suitable for recognition tasks where the images are taken as inputs. So, utilizing deep learning algorithms for recognition of Hindi script characters with the projected scheme will improve the accuracy of the system.KeywordsPattern recognitionCharacter classificationCNNAnd augmented data

Computer vision has penetrated many domains, for instance, security, sports, health and medicine, agriculture, transportation, manufacturing, retail, and so like. One of the computer vision tasks is character recognition. In this work, a visually similar handwritten Chinese character dataset is collected. Subsequently, an enhanced convolutional neural network is proposed for the recognition of visually similar handwritten Chinese characters. The convolutional neural network is enhanced by the dropout regularization and early stopping mechanism to reduce the overfitting problem. The Adam optimizer is also leveraged to accelerate and optimize the training process of the convolutional neural network. The empirical results demonstrate that the enhanced convolutional neural network achieves a 97% accuracy, thus corroborate it has better discriminating power in visually similar handwritten Chinese character recognition.