Gradient Local Auto-Correlation for handwritten Devanagari character recognition

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.

In this paper we propose a Least Mean Square (LMS) algorithm for the practical training of the class of min-max classifiers. These are lattice-theoretic generalization of Boolean functions and are also related to feed-forward neural networks and morphological signal operators. We applied the LMS algorithm to the problem of handwritten character recognition. The database consists of segmented and cleaned digits. Features that were extracted from the digits include Fourier descriptors and morphological shape-size histograms. Experimental results using the LMS algorithm for handwritten character recognition are promising. In our initial experimentation, we applied the min-max classifier to binary classification of '0' and '1' digits. By preprocessing the feature vectors, we were able to achieve an error rate of 1.75% for a training set of size 1200 (600 of each digit); and an error rate of 4.5% on a test set of size 400 (200 of each). These figures are comparable to those obtained by 2-layer neural nets trained using back propagation. The major advantage of min-max classifiers compared to neural networks is their simplicity and the faster convergence of their training algorithm.

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 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 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%.

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.

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.

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.

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

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.

The entirely distinct pattern recognition technologies have been proposed over recent years and thus the different research teams focus on the effects of popularity. Because of its use in many areas, such as pattern recognition and machine learning, handwritten character recognition has found great success. In online handwritten character recognition, the basic field is for use. The various character recognition techniques were suggested in the offline handwritten recognition process. Although the techniques for transforming textual content are established by some empirical studies and publications. This textual material has been translated from a paper file into a machine-readable form. The character recognition system could help produce a paperless document as a key in the coming days. The key aspect was the digitization of paper documents and the retrieval of existing paper records as well. In this job, we took out offline samples of some Rajasthani handwritten characters. The proposed average recognition rate for machine archives is 89.82 percent, using histogram oriented gradient features and support vector machine classifiers.

The problem of Optical Character Recognition is as follows- here input is a scanned image or a printed or handwritten text and the output is a computer readable version of the input content. A lot of research work has been done on OCR worldwide for different languages. Though ‘Bengali’ is the second most popular script and language in Indian subcontinent and the fifth most popular language in the world, a fewer no. of research publications are there. The application areas of character recognition are increasing remarkably. Here we have proposed a new approach of handwritten Bengali character recognition using Graph Based Chain Code Method. Our aim is to achieve maximum recognition rate with minimum processing time.

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.

In the present chapter, the widely common problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform and artificial neural networks. The technique has been tested and found to be more accurate and economic in respect of the recognition process time of the system. Features of the handwritten character images are extracted by discrete wavelet transform used with appropriate level of multiresolution technique, then the artificial neural networks is trained by extracted features. The unknown input handwritten character images are recognized by trained artificial neural networks system. The proposed method provides good recognition accuracy for handwritten characters with less training time, less no. of samples and less no. of iterations.

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%.