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From the empirical studies, it is quite difficult for the license plate recognition to perform 100% accuracy in a real world environment. Nevertheless, it is common that only a few characters are misread from a license plate recognition system. In this paper, license plate matching is used for vehicle re-identification. We evaluate several approximate string matching techniques to determine an applicable...
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, an information fusion system for tree species recognition through leaves is proposed. This approach consists in training sub-classifiers (Random forests) with attributes extracted from leaf photos. The database is incomplete, partial and some data is conflicting. A hierarchical fusion system based on Belief functions theory allows the fusion of data provided by different sub-classifiers...
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...
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...
This paper addresses the problem of holistic recognition of printed ligatures in Nastalique writing style of the Urdu language. The main difficulty of the recognition process lies in the large number of classes/ligatures (17,000 different possible ligatures in our Urdu text data). This large number of classes not only limits the efficiency (run-time) of the recognition algorithms, but also makes it...
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)...
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...
The studies of Optical Character Recognition (OCR) are being developed since it still needs a performance improvement. The previous study of alphanumeric character recognition had been conducted by Blumenstein and Liu using Modified Direction Feature (MDF) and Multi Layer Perceptrons (MLP) network. The study reaches the accuracy rate of 70.22% for lowercase characters and 80.83% for uppercase characters.
Comparing with conventional character normalization methods not taking the discriminative information into account, this paper proposes a novel normalization method — Discriminative Normalization. Saliency regions contain most of discriminative information among similar characters. According to different types, they are enlarged in character normalization to increase their influence in recognition...
Reading text from scene images is a challenging problem that is receiving much attention, especially since the appearance of imaging devices in low-cost consumer products like mobile phones. This paper presents an easy and fast method to recognize individual characters in images of natural scenes that is applied after an algorithm that robustly locates text on such images. The recognition is based...
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...
Characters extracted from images or graphics pose a challenge for traditional character recognition techniques. The high degree of intraclass variation along with the presence of clutter makes accurate recognition difficult, yet the semantic information conveyed by sections of text within images or graphics makes their recognition an important problem. Previous work has shown that, on the two most...
We analyze in this paper the impact of sub-models choice for automatic Arabic printed text recognition based on Hidden Markov Models (HMM). In our approach, sub-models correspond to characters shapes assembled to compose words models. One of the peculiarities of Arabic writing is to present various character shapes according to their position in the word. With 28 basic characters, there are over 120...
In this paper, we present an innovative approach to integrate spatial relations in stroke clustering for handwritten Devanagari character recognition. It handles strokes of any number and order, writer independently. Learnt strokes are hierarchically agglomerated via Dynamic Time Warping based on their location and their number and stored accordingly. We experimentally validate our concept by showing...
In this paper, we describe Jolly mate, a product concept that we have envisioned as assistive technology for young children with Dyslexia. Jolly mate, a digital notepad, emulates the Jolly Phonics system of teaching letter sounds and letter formation to children with dyslexia. Jolly mate in turn uses simple handwritten character recognizers created using the Lipi IDE tool from the Lipi Toolkit project,...
Cursive scripts such as Urdu, Pashto and Arabic contain large number of unique shapes called ligatures. Recognition of thousands of ligatures is challenging due to variations of various kinds including scaling, orientation, font style, spatial location/registration of ligatures and limited number of samples available for training. Accurate segmentation is a key challenge for analytic approaches, whereas...
Aiming at the seeds of biological stability genetic character, we present a method to feature extraction based on visual invariance. By analyzing the weed seeds and hilum shape characteristics, nine shape features and seven moment invariants of visual invariance were extracted. Back Propagation (BP) Neural Network was used to identify weed seeds, and the relationship between the change of features...
This paper considers the topic of automatic font recognition. The task is to recognize a specific font from a text snippet. Unlike previous contributions, we evaluate, how the frequencies of certain letters or words influence automatic recognition systems. The evaluation provides estimates on the general feasibility of font recognition under various changing conditions. Results on a data-set containing...
In this paper, we consider the problem of automatically reading graffiti tags. As a preparatory step, we create a large set of synthetic graffiti-like characters, generated from publicly available true type fonts. For each character in the database, we extract a number of scale independent local binary descriptors. Then, using binary non negative matrix factorization, a sufficient number of basis...
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