Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features

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.

This paper gives a new approach for recognition of handwritten Devanagari characters. Twenty handwritten characters from 100 people resulting 2000 characters are used for the experimentation. The handwritten characters written of paper is scanned, preprocessed and on every individual characters wavelet transform is applied so as to get decomposed images of characters. Statistical parameters are computed over the decomposition to form feature vector. The feature vectors serve as input to back propagation neural networks for classification into one of 20 classes and based classes they are recognized. The accuracy obtained is around 70 percent over large number of samples.

Handwritten character recognition represents a major challenge in the fields of pattern recognition and machine learning, it is more concerned especially in the case of regional languages like Kannada. Kannada, a prominent Dravidian script, poses distinct challenges because of its intricate character formations and diverse handwriting styles. This study introduces a method for identifying handwritten Kannada characters with the application of three machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM). The dataset utilized comprises an extensive array of handwritten Kannada characters sourced from the Kaggle repository. Preprocessing techniques, including image standardization and noise reduction, which are implemented to enhance recognition accuracy. CNN is used for feature extraction as well as classification, while KNN and SVM serve for comparative evaluation. The performance of these models is assessed using metrics such as precision, accuracy, recall, and F1-score. Findings indicate that CNN surpasses KNN and SVM in recognition accuracy, highlights the advantages of deep learning in the classification of handwritten characters. Additionally, a web-based app which is developed using Django to facilitate real-time character recognition. This research significantly contributes to the progress of Optical Character Recognition in Kannada script, promoting advancements in digitization and automation.

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.

This paper presents the development of feature extraction algorithms for the recognition of off-line handwritten characters. These algorithms are used to exploit prominent features of characters. The decision trees were used to classify characters that share common features into five classes then 12 algorithms were developed. As a result, the major features of characters such as an end-point (EP), a turning point (TP), a loop (LP), a zigzag (ZZ), a closed top (CT), a closed bottom (CB), and a number of legs were identified. These features were defined as standard features or the Thai Character Feature Space. Then, we defined the 5x3 standard regions used to map these standard features, result in the Thai Character Solution Space, which will be used as a fundamental tool for recognition. The algorithms have been tested thoroughly by using of more than 44,600 characters handwritten by 22 individuals from 100 documents. The feature extraction rate is as high as 98.66% with the average of 93.08% while the recognition rate is as high as 99.19%1 with the average of 91.42%. The results indicate that our proposed algorithms are well established and effective.

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.

This paper proposes a hidden Markov mesh random field (HMMRF)-based stochastic model for off-line handwritten Chinese characters recognition using statistical observation sequences embedded in the strokes of a character. Due to the great amount of Chinese characters and many different writing styles or variations, the recognition of handwritten Chinese characters becomes more difficult and challenging than any other character recognition. In our approach, a new framework based on HMMRF model is put forward at first. The estimation of model parameters and state sequence decoding algorithms are also discussed later. Besides the mathematical model and corresponding issues, nonlinear shape normalization scheme that modifies the distortion and adjusts the correlation of strokes is applied. Two types of stroke-based features are extracted for the rough classification and observation sequence respectively. Experimental results on isolated handwritten Chinese characters demonstrate the effectiveness of our approach.

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

Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

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.

Off-line handwritten Korean character recognition based on stroke extraction and representation

Character recognition has been an active area of research and, due to its diverse applicable environment, it continues to be a challenging research topic. The data entry form is a convenient and successful tool for collection of information by filling the sheets with handwritten characters. For many purposes, such as documenting and archiving, extracting the handwritten characters is important. One of the most important fields in these forms is the data-filled boxes. The extraction process is important in processes of handwritten recognition techniques. Feature extraction plays an important role in different classification-based problems, such as face recognition, signature verification, optical character recognition (OCR), etc. The performance of OCR highly depends on the proper selection and extraction of a feature set. Different feature extraction methods are designed for different representations of the characters. The holistic approach is focused on feature extraction of the entire image and recognition using a neural network. This work proposes a subspace approach that regularizes and extracts Eigen features from handwritten numerals. Extracted Eigen features are recognized using a neural network. The proposed algorithm has been successfully implemented and has the added advantage of obtaining the extraction and recognition result at the same time.

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%.

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%.