Handwritten Devanagari character recognition using wavelet based feature extraction and classification scheme

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.

The large data set and similar structural features of the characters in Devanagari script demand a highly efficient classification and recognition system. This paper presents a novel approach for the recognition of unconstrained handwritten Devanagari characters. The system is based on multi-stage classification scheme. The classification stages categorize the characters into smaller groups. The classification is done using two stages, first stage is based on fuzzy inference system and second stage is based on structural parameters. The fuzzy system improves the classification over crisp classification. The classified characters are passed to the feature extraction stage. The final stage implements feed forward neural network for character recognition. The recognition accuracy achieved by the proposed method is 96.95%.

Offline handwritten character recognition is the process of recognizing given characters from the large set of characters. OCR system mainly focuses on the recognition of printed or handwritten characters of a scanned image. The proposed system extracts features that are based only on gradient of image which is helpful in exact recognition of characters. A technique to recognize handwritten Devanagari characters using combination of quadratic and SVM classifiers is presented in this paper. Features used are directional features that are strength, angle and histogram of gradient (SOG, AOG, HOG). Using a Gaussian filter, the strength and the angle features are down sampled to obtain a feature vector of 392 dimensions. These features are finally concatenated with HOG feature. Applying these to the combination of quadratic and SVM classifiers to obtain maximum accuracy of 95.81% using 3 fold cross validation.

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.

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 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.

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.

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.

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%.

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

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.

Efficient methods have been proposed in the literature for recognition of printed characters of Indian scripts. However, much less attention has been received for handwritten characters of Indian scripts with only a few work available in the literature. In this paper, a novel zone based method has been presented for recognition of handwritten characters written in Kannada language, a major South Indian language. The normalized character image is divided into 64 zones each of size 8x8 pixels. For each zone, from left to right and from top to bottom, the crack code, representing the line between the object pixel and the background (the crack), is generated by traversing it in anticlockwise direction. A feature vector of size 512 is obtained for each character. A multi-class SVM is used for the classification purpose. Experiments are performed on handwritten Kannada character images consisting of 24500 images with 500 samples of each character. Five-fold cross validation is used for result computation that yielded 87.24% recognition accuracy.

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.

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%.

Although various hand-written Kanji character recognition techniques have been developed, they are not yet satisfactory. Hand-written characters can vary in shape so much that the recognition rate depends greatly on their quality. It is very useful to know how humans recognize hand-written characters. Accordingly, we made two kinds of evaluations. First, we carried out a subjective experiment to ascertain whether humans use common criteria for judging character quality or not. Japanese adults were asked to rate the character quality on a 5 point- scale. The results suggested that the subjects used common criteria. Second, to find the character' key parts, we analyzed the subject's eye movement as the quality was evaluated. The experiments revealed that the parts on which the subject's attention was concentrated were: areas of high stroke density, the positioning of the stroke and its spacing, and character composition. Based on the results of these experiments, we have proposed new measures for stroke, pixel, line width, aspect ratio, and character balance. It was indicated that the measures are useful factors for determining the quality of hand-written Kanji characters.