The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this...
In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part, the study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm and fed by several parameter vectors; the objective of this operation is to determine...
In this paper, we present our recent study of a data driven approach to combining multiple SVM classifiers with RBF kernels each being trained with a distinct feature vector. The SVM classifiers in our ensemble are ranked based on their increasing order of average performance on the validation sample sets. The outputs of the SVM classifiers are combined based on a weighted average strategy which uses...
In this paper we address the problem of recognizing Farsi handwritten words. We extract two types of features from vertical stripes on word images: chain-code of word boundary and distribution of foreground density across the image word. The extracted feature vectors are coded using self organizing vector quantization. The result codes are used for training the model of each word in the database....
Most of existing works on online text-independent writer identification follow analytical approach based on grapheme or character as primitive. However, segmentation is time-consuming and becomes an obstacle in real time application systems. To avoid this problem, we propose a novel framework which follows global approach based on word as primitive. Different sets of statistic and dynamic features...
This paper presents a method of modern deep machine learning and its application in dimension reduction and lossy compression. Deep belief networks (or DBN's), first proposed by Yoshua Bengio, are hierarchic, stochastic, neural networks with appropriate architecture and dedicated training algorithms. They are composed of layers, each one of which is a restricted Boltzmann machine (or RBM). There exists...
This paper presents a novel feature extraction technique, called locality-based linear discriminant projection (LLDP), for multi-class discriminant tasks. The proposed method can be regarded as an improved version of the classical linear discriminant projection (LDA), which is one of the most popular feature extraction methods. LLDP has at least two distinct advantages compared with LDA. Firstly,...
This paper presents a simple and effective technique for the recognition of writer-independent offline handwritten Arabic Digits. The system is based on labeling the white pixels in a digit's image into nine different concavity categories. Four different feature vectors are extracted from these labeled concavities. Each feature vector is then introduced to a linear SVM classifiers. The final decision...
Feature extraction is an important research topic in the field of pattern recognition. The class-specific idea tends to recast a traditional multi-class feature extraction and recognition task into several binary class problems, and therefore inevitably class imbalance problem, where the minority class is the specific class, and the majority class consists of all the other classes. However, discriminative...
In this paper, we present two text-independent writer identification methods in a closed-world context. Both methods use on-line and off-line features jointly with a classifier inspired from information retrieval methods. These methods are local, respectively based on the character and grapheme levels. This writer identification engine may be used to personalize our cursive word recognition engine~\cite{icfhr2010}...
A dataset containing 26,720 handwritten legal amount words written in Hindi and Marathi languages (Devanagari script) is presented in this paper along with a training-free technique to recognize such handwritten legal amounts present on Indian bank cheques. The recognition of handwritten legal amount words in Hindi and Marathi languages is a challenging because of the similar size and shape of many...
In this paper we present a novel approach for fast search of handwritten Arabic word-parts within large lexicons. The algorithm runs through three steps to achieve the required results. First it warps multiple appearances of each word-part in the lexicon for embedding into the same euclidean space. The embedding is done based on the warping path produced by the Dynamic Time Warping (DTW) process while...
Recently, we have proposed a handwriting Chinese character database HIT-OR3C. Though it has been introduced in detail, to date, it has not been evaluated by any handwriting recognition method. To help the researchers use this database for algorithm evaluation, we propose the structure of HIT-OR3C database. Moreover, we evaluate the OR3C database with a series of experiments using state-of-the-art...
In this paper we designed an adaptation module (AM) with the objective to increase the performance of a recognition system for a new user or new writing style. The developed adaptation module is added after the recognition system, and its role is to examine the output of the independent system and produce a more correct output vector close to the desired response of the user. To achieve this end,...
This paper describes an unconstrained on-line handwritten Japanese text retrieval system from character recognition candidates. The system is based on a discriminative model which integrates the scores of character recognition, segmentation and geometric context in search and retrieval, and the parameters are trained by supervised learning. Experiments on TUAT Kuchibue database show that the proposed...
In this paper, we propose a new method of representation of on-line signatures by clustering of signatures. Our idea is to provide better representation by clustering of signatures based on global features. Global features of signatures of each cluster are used to form an interval valued feature vector which is a symbolic representation for a cluster. Based on cluster representation, we propose methods...
This work presents experimental results on online recognition of handwritten signature. The system makes use of dynamic data such as trajectory, pen velocity, pen pressure, pen azimuth, and pen altitude collected at the time of signing. In order to evaluate the effectiveness of the system several experiments are carried out. Online signature database from signature verification competition (SVC) 2004...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.