623,285 research outputs found

    An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

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    Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure

    Rhetorical Self-Fashioning in Aramburu: A Contemporary Take on Cervantine Techniques

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     In Cervantes’ Don Quijote (1605), Dulcinea does not participate in any dialogue, and yet still appears a vivid character as real as the other female characters who do speak in the novel. Dulcinea, a figment of others’ imaginations, forms a sharp contrast with the character of Marcela, who fashions an authoritative self through dialogue with other characters. Marcela’s self, like Dulcinea’s, is relational to others’; however, her self-fashioning frees her from the objectification of Dulcineism and instead Marcela makes herself a character that transgresses conventional narratives, both cultural and literary. Likewise, a similar rejection of Dulcineism and a desire to craft her life story through her dialectical exchanges with the rest of the characters enables Miren, a principal female character in Aramburu’s Patria (2016), to fashion a self that actively contravenes the general perspective of her son’s supposed crimes as an etarra. In this analysis, I consider the Cervantine technique of rhetorical self-fashioning in characters such as Marcela and I trace this technique in the development of the character of Miren in Aramburu’s contemporary novel, Patria. Cervantes’ Marcela inaugurates the self-fashioning character in Western fiction, which is expanded by Aramburu four-hundred years later with his female character, Miren. Like Marcela, Miren must fashion herself against a polyphony of voices, frequently male, that provide a variety of narratives shaping the events of her life. I argue that Aramburu’s character, like Cervantes’, is empowered to author her own narrative to contravene an undesired outcome. Furthermore, both female characters use dialogue to reject the common literary tendency towards Dulcineism and, through relational rhetoric, disregard conventional narratives in favor of creating their own: a remarkable choice for female characters, both then and now

    Dominant weight multiplicities in hybrid characters of Bn, Cn, F4, G2

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    The characters of irreducible finite dimensional representations of compact simple Lie group G are invariant with respect to the action of the Weyl group W(G) of G. The defining property of the new character-like functions ("hybrid characters") is the fact that W(G) acts differently on the character term corresponding to the long roots than on those corresponding to the short roots. Therefore the hybrid characters are defined for the simple Lie groups with two different lengths of their roots. Dominant weight multiplicities for the hybrid characters are determined. The formulas for "hybrid dimensions" are also found for all cases as the zero degree term in power expansion of the "hybrid characters".Comment: 15 page

    Learning Character-level Compositionality with Visual Features

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    Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.Comment: Accepted to ACL 201
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