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This paper presents a methodology for recognition of handwritten Marathi and English Characters-Numerals using shape context descriptor. During pre-processing an algorithm is developed to extract the Marathi and English Characters-Numerals form grid formatted datasheets. The corresponding sample points around the boundary of a character are computed. This is followed by obtaining the centroid of the...
In this paper, we propose a large vocabulary Mongolian offline handwriting recognition system, using hidden Markov models (HMMs)-deep neural networks (DNN) hybrid architectures which shows superior performance on auto speech recognize (ASR) tasks. We select 50 sub-characters from all shape of Mongolian letters as the smallest modeling unit. First, a set of intensity features are extracted from each...
This paper presents a new representation for handwritten math formulae: a Line-of-Sight (LOS) graph over handwritten strokes, computed using stroke convex hulls. Experimental results using the CROHME 2012 and 2014 datasets show that LOS graphs capture the visual structure of handwritten formulae better than commonly used graphs such as Time-series, Minimum Spanning Trees, and k-Nearest Neighbor graphs...
A frequency count based two stage classification approach is proposed by combining generative and discriminative modeling principles for online handwritten character recognition. The first stage classifier based on Hidden Markov Model (HMM) returns top-K ranking characters out of the total N classes. In the second stage, pairwise classifiers for K(K − 1)/2 unique combinations of top-K characters using...
Hidden Markov Models (HMM) are used in handwritten strokes recognition task. The two design parameters of HMM are the number of states and number of mixtures in each state. There are two approaches for finding the number of states, namely, equal number of states and variable number of states. Since the shape of strokes will be different, variable number of states approach should be beneficial. This...
In the context of handwritten mathematical expressions recognition, a first step consist on grouping strokes (segmentation) to form symbol hypotheses: groups of strokes that might represent a symbol. Then, the symbol recognition step needs to cope with the identification of wrong segmented symbols (false hypotheses). However, previous works on symbol recognition consider only correctly segmented symbols...
This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work is based on a new set of questions for the CART tree construction based on a "lossy mapping" categorization of the Arabic shapes. The used system is a combination of Hidden...
Sub-character HMM models for Arabic text recognition allow sharing of common patterns between different position-dependent shape forms of an Arabic character as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact, efficient, and robust recognizer with reduced model set. In the current paper...
Lampung Script is a non-cursive script where a rich set of diacritics is used to modify the syllable denoted by a character symbol. Consequently, the analysis of the relation between characters and diacritic marks associated with them plays an important role in the recognition process. As diacritics can appear in three different relative positions with respect to a character (top, bottom, and right)...
HMM-based analytical methods have been widely used for Arabic handwriting recognition. A key factor influencing the performance of HMM-based systems is the features extracted from a sliding window. In this paper, we propose a novel baseline-independent feature set extracted from a wider sliding window to directly capture the contextual information. This feature set is a combination of center of mass...
Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented...
Automatic off-line Arabic handwriting recognition based on segmentation still faces big challenges. A database, covering all shapes of handwritten Arabic characters, is required to facilitate the recognition process. This paper introduces a new database for handwritten Arabic characters (HACDB), designed to cover all shapes of Arabic characters including overlapping ones. It contains 6,600 shapes...
We propose a simple method for learning a dictionary of deformable patches for simultaneous shape recognition and reconstruction. Our approach relies on two key innovations - introducing a pre-defined set of transformations on patches to enrich the search space, and designing a parallel framework on Graphical Processors (GPUs) for matching a large number of deformable templates to a large set of images...
One-class classification is an important problem with applications in several different areas such as outlier detection and machine monitoring. In this paper we propose a novel method for one-class classification which also implements prototype reduction. The main feature of the proposed method is to analyze every limit of all the feature dimensions to find the true border which describes the normal...
We present a novel method of offline whole-word handwriting recognition. We use automatic image morphing to compute 2-D geometric warps that align the strokes of each word image with the strokes of word images of training examples. Once the strokes of a given word are aligned to a training example, we use distance maps to compare how similar the two words are. Like 1-D Dynamic Programming (DP) methods,...
The paper presents a method for isolated off-line character recognition using radon features. The key characteristic of the method is to use DTW algorithm to match corresponding pairs of radon histograms at every projecting angle. Thanks to DTW, it avoids compressing feature matrix into a single vector which may miss information. Comparison has been made with the state-of-the-art of shape descriptors...
In this paper, we present a template based approach to the segmentation of touching components in handwritten text lines. Local patches around touching components are identified and a dictionary is created consisting of template patches together with their correct segmentations. We use two shape context based methods to compute similarity between input patches and dictionary templates to find the...
Relative positioning between components of a structured object plays a key role for its interpretation. Fuzzy relative positioning templates are a description framework for 2D handwritten patterns, that is based on positioning models specifically designed for dealing with variability and imprecision of handwriting. In this work, we present fuzzy positioning templates and investigate the idea of recognizing...
The selection of the classifier architecture is a very important step in the recognition process. This paper presents a new algorithm for the HMMs architectures optimization: Multi-Models Evolvement using PSO (MME-PSO). The proposed algorithm is applied to an Arabic handwriting recognition system. The recognizer is based on character Hidden Markov Models which can have different architectures. This...
This competition scenario aims at a performance comparison of several automated systems for the task of signature verification. The systems have to rate the probability of authorship and non-authorship of signatures. In particular they have to determine whether questioned signatures are simulated disguised or the normal signature of the reference writer. Furthermore, the results will be compared to...
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