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  • 1
    ISSN: 1433-2825
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1433-2825
    Keywords: Key words:Offline recognition – Cursive script recognition – Perception – Reading model – Activation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. This paper presents a model for reading cursive scripts which has an architecture inspired by the behavior of human reading and perceptual concepts. The scope of this study is limited to offline recognition of isolated cursive words. First, this paper describes McClelland and Rumelhart's reading model, which formed the basis of the system. The method's behavior is presented, followed by the main original contributions of our model which are: the development of a new technique for baseline extraction, an architecture based on the chosen reading model (hierarchical, parallel, with local representation and interactive activation mechanism), the use of significant perceptual features in word recognition such as ascenders and descenders, the creation of a fuzzy position concept dealing with the uncertainty of the location of features and letters, and the adaptability of the model to words of different lengths and languages. After a description of our model, new results are presented.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1433-2825
    Keywords: Key words:Optical character recognition – Ground truth – Nelder-Mead algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. Since optical character recognition systems often require very large amounts of training data for optimum performance, it is important to automate the process of finding ground truth character identities for document images. This is done by finding a transformation that matches a scanned image to the machine-readable document description that was used to print the original. Rather than depend on finding feature points, a more robust procedure is to follow up by using an optimization algorithm to refine the transformation. The function to optimize can be based on the character bounding boxes – it is not necessary to have access to the actual character shapes used when printing the original.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1433-2825
    Keywords: Key words:Optical character recognition – Feature – Distance features – Map tiling – Discriminative power
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. Features play an important role in OCR systems. In this paper, we propose two new features which are based on distance information. In the first feature (called DT, Distance Transformation), each white pixel has a distance value to the nearest black pixel. The second feature is called DDD (Directional Distance Distribution) which contains rich information encoding both the black/white and directional distance distributions. A new concept of map tiling is introduced and applied to the DDD feature to improve its discriminative power. For an objective evaluation and comparison of the proposed and conventional features, three distinct sets of characters (i.e., numerals, English capital letters, and Hangul initial sounds) have been tested using standard databases. Based on the results, three propositions can be derived to confirm the superiority of both the DDD feature and the map tilings.
    Type of Medium: Electronic Resource
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  • 5
    ISSN: 1433-2825
    Keywords: Key words:Signature verification – Fourier descriptors – Hidden Markov Models – Viterbi decoding
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. A method for the automatic verification of online handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that adding a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), to the global features of a signature, significantly improves the performance of verification. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.
    Type of Medium: Electronic Resource
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  • 6
    ISSN: 1433-2825
    Keywords: Key words:Document classification – Feature selection – Learning – OCR –N-grams
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. In the literature, many feature types are proposed for document classification. However, an extensive and systematic evaluation of the various approaches has not yet been done. In particular, evaluations on OCR documents are very rare. In this paper we investigate seven text representations based on n-grams and single words. We compare their effectiveness in classifying OCR texts and the corresponding correct ASCII texts in two domains: business letters and abstracts of technical reports. Our results indicate that the use of n-grams is an attractive technique which can even compare to techniques relying on a morphological analysis. This holds for OCR texts as well as for correct ASCII texts.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    ISSN: 1433-2825
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Type of Medium: Electronic Resource
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  • 8
    ISSN: 1433-2825
    Keywords: Key words:Arabic characters – Optical character recognition – Segmentation – Cursive script – Degraded text recognition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. In recognizing cursive scripts, a major undertaking is segmenting cursive words into characters and isolating merged characters. The segmentation is usually the pivotal stage in the system to which a sizable portion of processing is devoted and a considerable share of recognition errors is attributed. The most notable feature of Arabic writing is its cursiveness. Compared to other features, the cursiveness of Arabic words poses the most difficult problem for recognition algorithms. In this work, we describe the design and implementation of an Arabic word recognition system. To recognize a word, the system does not segment it into characters in advance; rather, it recognizes the input word by detecting a set of “shape primitives” on the word. It then matches the regions of the word (represented by the detected primitives) with a set of symbol models. A spatial arrangement of symbol models that are matched to regions of the word, then, becomes the description of the recognized word. Since the number of potential arrangements of all symbol models is combinatorially large, the system imposes a set of constraints that pertain to word structure and spatial consistency. The system searches the space made up of the arrangements that satisfy the constraints, and tries to maximize the a posteriori\/ probability of the arrangement of symbol models. We measure the accuracy of the system not only on words but on isolated characters as well. For isolated characters, it has a recognition rate of 99.7% for synthetically degraded symbols and 94.1% for scanned symbols. For isolated words the system has a recognition rate of 99.4% for noise-free words, 95.6% for synthetically degraded words, and 73% for scanned words.
    Type of Medium: Electronic Resource
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  • 9
    ISSN: 1433-2825
    Keywords: Key words:Map image processing – Segmentation – Land use maps – Clustering – Filtering
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. One important step in the analysis of digitized land use map images is the separation of the information in layers. In this paper we present a technique called Selective Attention Filter which is able to extract or enhance some features of the image that correspond to conceptual layers in the map by extracting information from results of clustering of local regions on the map. Different parameters can be used to extract or enhance different information on the image. Details on the algorithm, examples of application of the filter and results are also presented.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    ISSN: 1433-2825
    Keywords: Key words: Word recognition – Character shape coding – Lexical contents – Lexical specificity – Template matching
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. We describe a process of word recognition that has high tolerance for poor image quality, tunability to the lexical content of the documents to which it is applied, and high speed of operation. This process relies on the transformation of text images into character shape codes, and on special lexica that contain information on the shape of words. We rely on the structure of English and the high efficiency of mapping between shape codes and the characters in the words. Remaining ambiguity is reduced by template matching using exemplars derived from surrounding text, taking advantage of the local consistency of font, face and size as well as image quality. This paper describes the effects of lexical content, structure and processing on the performance of a word recognition engine. Word recognition performance is shown to be enhanced by the application of an appropriate lexicon. Recognition speed is shown to be essentially independent of the details of lexical content provided the intersection of the occurrences of words in the document and the lexicon is high. Word recognition accuracy is dependent on both intersection and specificity of the lexicon.
    Type of Medium: Electronic Resource
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