593,375 research outputs found

    COMPARATIVE STUDY OF FONT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS AND TWO FEATURE EXTRACTION METHODS WITH SUPPORT VECTOR MACHINE

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    Font recognition is one of the essential issues in document recognition and analysis, and is frequently a complex and time-consuming process. Many techniques of optical character recognition (OCR) have been suggested and some of them have been marketed, however, a few of these techniques considered font recognition. The issue of OCR is that it saves copies of documents to make them searchable, but the documents stop having the original appearance. To solve this problem, this paper presents a system for recognizing three and six English fonts from character images using Convolution Neural Network (CNN), and then compare the results of proposed system with the two studies. The first study used NCM features and SVM as a classification method, and the second study used DP features and SVM as classification method. The data of this study were taken from Al-Khaffaf dataset [21]. The two types of datasets have been used: the first type is about 27,620 sample for the three fonts classification and the second type is about 72,983 sample for the six fonts classification and both datasets are English character images in gray scale format with 8 bits. The results showed that CNN achieved the highest recognition rate in the proposed system compared with the two studies reached 99.75% and 98.329 % for the three and six fonts recognition, respectively. In addition, CNN got the least time required for creating model about 6 minutes and 23- 24 minutes for three and six fonts recognition, respectively. Based on the results, we can conclude that CNN technique is the best and most accurate model for recognizing fonts

    The feedback consistency effect in Chinese character recognition:evidence from a psycholinguistic norm

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    Researchers have demonstrated the importance of phonology in literacy acquisition and in visual word recognition. For example, the spelling-to-sound consistency effect has been observed in visual word recognition tasks, in which the naming responses are faster and more accurate for words with the same letters that also have the same pronunciation (e.g. -ean is always pronounced /in/, as in lean, dean, and bean). In addition, some studies have reported a much less intuitive feedback consistency effect when a rime can be spelled in different ways (e.g. /ip/ in heap and deep) in lexical decision tasks. Such findings suggest that, with activation flowing back and forth between orthographic and phonological units during word processing, any inconsistency in the mappings between orthography and phonology should weaken the stability of the feedback loop, and, thus, should delay recognition. However, several studies have failed to show reliable feedback consistency in printed word recognition. One possible reason for this is that the feedback consistency is naturally confounded with many other variables, such as orthographic neighborhood or bigram frequency, as these variables are difficult to tease apart. Furthermore, there are challenges in designing factorial experiments that perfectly balance lexical stimuli on all factors besides feedback consistency. This study aims to examine the feedback consistency effect in reading Chinese characters by using a normative data of 3,423 Chinese phonograms. We collected the lexical decision time from 180 college students. A linear mixed model analysis was used to examine the feedback consistency effect by taking into account additional properties that may be confounded with feedback consistency, including character frequency, number of strokes, phonetic combinability, semantic combinability, semantic ambiguity, phonetic consistency, noun-to-verb ratios, and morphological boundedness. Some typical effects were observed, such as the more frequent and familiar a character, the faster one can decide it is a real character. More importantly, the linear mixed model analysis revealed a significant feedback consistency effect while controlling for other factors, which indicated that the pronunciation of phonograms might accommodate the organization of Chinese orthographic representation. Our study disentangled the feedback consistency from the many other factors, and supports the view that phonological activation would reverberate to orthographic representation in visual word recognition

    Discriminative Block-Diagonal Representation Learning for Image Recognition

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    Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intraclass representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets, three character data sets, and the 15 scene multicategories data set. It not only shows superior potential on image recognition but also outperforms the state-of-the-art methods

    Are you reading what I am reading? The impact of contrasting alphabetic scripts on reading English

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    This study examines the impact of the crosslinguistic similarity of translation equivalents on word recognition by Russian-English bilinguals, who are fluent in languages with two different but partially overlapping writing systems. Current models for bilingual word recognition, like BIA+, hold that all words that are similar to the input letter string are activated and considered for selection, irrespective of the language to which they belong (Dijkstra and Van Heuven, 2002). These activation models are consistent with empirical data for bilinguals with totally different scripts, like Japanese and English (Miwa et al., 2014). Little is known about the bilingual processing of Russian and English, but studies indicate that the partially distinct character of the Russian and English scripts does not prevent co-activation (Jouravlev and Jared, 2014; Marian and Spivey, 2003; Kaushanskaya and Marian, 2007)

    A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition

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    An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications

    The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique

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    The arrival of handwriting recognition technologies offers new possibilities for research in heritage studies. However, it is now necessary to reflect on the experiences and the practices developed by research teams. Our use of the Transkribus platform since 2018 has led us to search for the most significant ways to improve the performance of our handwritten text recognition (HTR) models which are made to transcribe French handwriting dating from the 17th century. This article therefore reports on the impacts of creating transcribing protocols, using the language model at full scale and determining the best way to use base models in order to help increase the performance of HTR models. Combining all of these elements can indeed increase the performance of a single model by more than 20% (reaching a Character Error Rate below 5%). This article also discusses some challenges regarding the collaborative nature of HTR platforms such as Transkribus and the way researchers can share their data generated in the process of creating or training handwritten text recognition models

    Text localization and recognition in natural scene images

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    Text localization and recognition (text spotting) in natural scene images is an interesting task that finds many practical applications. Algorithms for text spotting may be used in helping visually impaired subjects during navigation in unknown environments; building autonomous driving systems that automatically avoid collisions with pedestrians or automatically identify speed limits and warn the driver about possible infractions that are being committed; and to ease or solve some tedious and repetitive data entry tasks that are still manually carried out by humans. While Optical Character Recognition (OCR) from scanned documents is a solved problem, the same cannot be said for text spotting in natural images. In fact, this latest class of images contains plenty of difficult situations that algorithms for text spotting need to deal with in order to reach acceptable recognition rates. During my PhD research I focused my studies on the development of novel systems for text localization and recognition in natural scene images. The two main works that I have presented during these three years of PhD studies are presented in this thesis: (i) in my first work I propose a hybrid system which exploits the key ideas of region-based and connected components (CC)-based text localization approaches to localize uncommon fonts and writings in natural images; (ii) in my second work I describe a novel deep-based system which exploits Convolutional Neural Networks and enhanced stable CC to achieve good text spotting results on challenging data sets. During the development of both these methods, my focus has always been on maintaining an acceptable computational complexity and a high reproducibility of the achieved results
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