Transfer learning based handwritten character recognition of tamil script using inception-V3 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.

Handwritten character recognition is playing a vital role in many areas of modern world. Even though considerable research work has been done in handwritten character recognition, comparatively fewer efforts have been made on handwritten Tamil character recognition. This paper proposes an adaptable method for recognizing handwritten Tamil characters. The adaptability is achieved using a type of artificial neural network called Kohonen self organizing maps (KSOM). In addition, a fine-tuning method, that uses global features, is admitted to fine tune the results. A demo for proposed concept is presented here, which is developed for a subset of Tamil alphabet. The demo gives a recognition accuracy of 86%.

Recognition of handwritten characters is one of the difficult and challenging task because of the variation of characters in size, style and pattern. The complexity increased further with dictionary (alphabets, numerals, special characters), more individuals, age groups, and also with working environment. The exploration of open-source pre-trained networks for the classification of characters was minimal. This motivated us to explore the pre-trained deep convolutional networks (Alexnet, VGG-16, Resnet-50), and fine-tune them to recognize the handwritten characters using transfer learning. The experimentation results of widely used database EMNIST using pre-trained networks are in-par with the results of the state-of-art customized networks,which is specific to database and language. The classification accuracy of Resnet-50 for EMNIST (By-class: 87.24%, By-merge: 90.64%, Balanced: 89.18%, Letters: 94.90%, Digits: 99.57%).

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.

This manuscript is focus on the utilization of object detection algorithm GLAC (Gradient Local Auto-Correlation) for the handwritten character recognition (HCR) problem. HOG and SIFT are already used in this (HCR) field except GLAC which produced good results than HOG and SIFT for object detection problem like human in images, pedestrian detection and image patch matching. This paper utilized GLAC algorithm to recognize the handwritten Devanagari characters. GLAC applied on two handwritten Devanagari databases, ISIDCHAR and V2DMDCHAR. The images of databases are also normalized with and without preserving aspect ratio. Using GLAC method and SVM classifier, the best results obtained on ISIDCHAR and V2DMDCHAR are 93.21%, 95.21 % respectively that justified the utilization of GLAC algorithm for character recognition problem.

Character recognition is developed for various patterns of handwritten or optical characters to be recognized digitally. There are many Tamil literatures in undigitized form. Using deep learning the undigitized Tamil literatures can be converted into readable format. Many researches were carried on character recognition using deep learning for languages like Arabic, Devanagari, Telugu, etc… Due to the larger category set and confusion in similarities between handwritten characters Tamil character recognition is a challenge. In this paper, we propose a character recognition system for handwritten Tamil characters using deep learning. Here, VGG 16 approaches is carried out. The proposed work gives efficiency of 94.52% on our datasets.

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.

Handwriting recognition has earned a fair amount of attention in the field of pattern recognition. A lot of work has been done so far for the efficient recognition of handwritten characters. So far, various techniques have been put forward for handwritten character recognition. However, not much work has been done yet for the recognition of Assamese handwritten characters because of their complex structure and limited usage. The Assamese language spoken in some of the northeastern parts of India is lagging in this field. This paper aims to make human Assamese handwriting machine-readable. Successful recognition will enable machines to read forms, postal addresses, and signboards written in Assamese. It will also lead to advancements in other areas such as translations of handwritten Assamese scripts to other languages as well as enable a text-to-speech system, thus making the Assamese language more acknowledgeable. This paper attempts to recognize handwritten Assamese characters using Convolution Neural Network (CNN) by collecting our own dataset. We have judged the performance of our system by the performance metrics: Precision, Recall, and F1 score.

Handwritten character recognition (HCR) is a pattern recognition problem. The HCR complication has been extensively learned for several decades, but there has been tiny research on independent script models. This is due to a variety of variables, lack of datasets, the concentration of most traditional research on character recognition approaches are language specific and unattainable. Convolutional Neural Networks are deep learning models that take input and process it through multiple layers to produce the desired output based on training. In order to increase the volume of the dataset, GAN (Generative Adversarial Network) framework was adapted on the HP Labs e TAMIL handwritten character dataset. Despite the fact that GAN improves the dataset, processing the images takes a long time, the proposed work hybrid the models Variational Autoencoder (VAE) and GAN model to speed up processing. A GAN tries to produce new data that cannot be identified from real data, whereas a VAE learns to encode the provided data and then reconstructs the new images from the encoding. IN VAE-GAN, we employ the latent representations produced by an encoder for a variety of purposes. The proposed system uses VAE-GAN to enlarge the dataset and Convolutional Neural Networks to recognise handwritten Tamil characters. The accuracy of VAE-GAN with CNN is found to be 86%.

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

Highly structured neural networks like the Time-Delay Neural Network (TDNN) can achieve very high recognition accuracies in real world applications like on-line handwritten character and speech recognition systems. Achieving the best possible performance greatly depends on the optimization of all structural parameters for the given task and amount of training data. We propose an Automatic Structure Optimization (ASO) algorithm that avoids time-consuming manual optimization and apply it to Multi State Time-Delay Neural Networks (MSTDNNs), a recent extension of the TDNN. We show that MSTDNNs are a very powerful approach to on-line handwritten character and word recognition and that the ASO algorithm can automatically structure this type of architecture efficiently in a single training run.KeywordsSpeech RecognitionState UnitHide UnitDynamic Time WarpingOutput UnitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition

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