La mujer
•Descargar como PPT, PDF•
0 recomendaciones•116 vistas
La mujer camina delante de su sombra y precede a la luz como las aves al solsticio. Ha guardado solo su juventud y las lágrimas engarzadas en piedras. Extiende su pelo sobre el árbol otoñal y es dócil a sus hojas. Sus manos son como las de un niño y ahora es visible en el silencio ofreciendo sus ojos al animal oscuro que mira mansamente. Ha estado con el autor desde el principio trazando la forma de las cosas con el agua oculta.
Denunciar
Compartir
Denunciar
Compartir
![[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recomendados
Día de la mujer (velastegui zambrano y basantes)
El Día Internacional de la Mujer se celebra el 8 de marzo para conmemorar la lucha de la mujer por la igualdad. Tiene su origen en 1911 cuando se realizó la primera convocatoria en varios países europeos. En 1977, la ONU lo proclamó como Día Internacional por los Derechos de la Mujer y la Paz Internacional. El tema de 2013 se centró en poner fin a la violencia contra las mujeres.
Mujer
El poema describe a una mujer que camina delante de su sombra, precedida por la luz como las aves en el solsticio. Es dócil a la naturaleza y ofrece sus ojos al animal oscuro que la mira mansamente. Ha estado con el poeta desde el principio y ha trazado la forma de las cosas con el agua oculta. El segundo párrafo habla de la belleza y fragilidad de la mujer, exhortándola a no dejar que el maltrato opaque su belleza ni la tristeza opaque sus ojos.
Mujer
El poema describe a una mujer que camina delante de su sombra, precedida por la luz como las aves en el solsticio. Se ha despojado de todo salvo su juventud y las lágrimas engarzadas en piedras. Ha extendido su pelo sobre un árbol otoñal y es dócil a las hojas. Sus manos son como las de un niño y ofrece sus ojos al animal oscuro que la mira mansamente. Ha estado con el autor desde el principio y ha trazado la forma de las cosas con el agua oculta.
De quien ha muerto
Palabras de alguien que esta por morir, asi comienza: "Cuando muera, da lo que queda de mí a los niños”. Si necesitas llorar, llora por los hermanos que caminan a tu lado. Rodea a alguien con tus brazos y dale lo que necesites darme a mí. Quiero dejarte con algo, algo mejor que palabras o sonidos.
Quién es quién Córdoba
El documento lista diferentes advocaciones marianas que son patronas de diversas ciudades y lugares. Menciona algunas advocaciones como la Virgen del Rosario, la Inmaculada, la Virgen de la Caridad, Nuestra Señora del Quinche, la Virgen de la Almudena, Nuestra Señora de la Cabeza, Nuestra Señora de Itatí, la Virgen de la Fuensanta, la Virgen de la Merced, la Virgen del Remedio y la Virgen de la Victoria. Luego proporciona pistas sobre el significado y características del nombre de
5 character classifiers
The document discusses character recognition techniques used in Tesseract. It describes how Tesseract extracts 3D feature vectors from characters and compares them to prototype vectors to classify characters. It also discusses various normalization techniques used, including character normalization to align and scale characters. The document compares Tesseract's approach to recent deep learning techniques and provides examples of how different normalizations are applied.
8 modernization efforts
Tesseract is modernizing its OCR capabilities by cleaning up code, adding newer technologies like OpenCL, and improving its classifier. OpenCL has accelerated Tesseract's processing pipeline by 36% so far. The plug-n-play classifier API allows new classifiers to be implemented. Tesseract is also exploring eliminating polygonal approximations and baseline normalization requirements. An LSTM integration is coming that will be fully integrated and allow 2D inputs and visualization.
Khmer ocr scientificday_itc
This document summarizes a Khmer optical character recognition (OCR) project. It discusses what OCR is and provides details about the Khmer OCR project team and their goal to develop a font and size independent Khmer OCR system. It reviews the state of existing Khmer OCR research and the components of the Khmer OCR system including preprocessing, segmentation, recognition, and postprocessing. It concludes with an overview of the current status of the project and future perspectives.
Recomendados
Día de la mujer (velastegui zambrano y basantes)
El Día Internacional de la Mujer se celebra el 8 de marzo para conmemorar la lucha de la mujer por la igualdad. Tiene su origen en 1911 cuando se realizó la primera convocatoria en varios países europeos. En 1977, la ONU lo proclamó como Día Internacional por los Derechos de la Mujer y la Paz Internacional. El tema de 2013 se centró en poner fin a la violencia contra las mujeres.
Mujer
El poema describe a una mujer que camina delante de su sombra, precedida por la luz como las aves en el solsticio. Es dócil a la naturaleza y ofrece sus ojos al animal oscuro que la mira mansamente. Ha estado con el poeta desde el principio y ha trazado la forma de las cosas con el agua oculta. El segundo párrafo habla de la belleza y fragilidad de la mujer, exhortándola a no dejar que el maltrato opaque su belleza ni la tristeza opaque sus ojos.
Mujer
El poema describe a una mujer que camina delante de su sombra, precedida por la luz como las aves en el solsticio. Se ha despojado de todo salvo su juventud y las lágrimas engarzadas en piedras. Ha extendido su pelo sobre un árbol otoñal y es dócil a las hojas. Sus manos son como las de un niño y ofrece sus ojos al animal oscuro que la mira mansamente. Ha estado con el autor desde el principio y ha trazado la forma de las cosas con el agua oculta.
De quien ha muerto
Palabras de alguien que esta por morir, asi comienza: "Cuando muera, da lo que queda de mí a los niños”. Si necesitas llorar, llora por los hermanos que caminan a tu lado. Rodea a alguien con tus brazos y dale lo que necesites darme a mí. Quiero dejarte con algo, algo mejor que palabras o sonidos.
Quién es quién Córdoba
El documento lista diferentes advocaciones marianas que son patronas de diversas ciudades y lugares. Menciona algunas advocaciones como la Virgen del Rosario, la Inmaculada, la Virgen de la Caridad, Nuestra Señora del Quinche, la Virgen de la Almudena, Nuestra Señora de la Cabeza, Nuestra Señora de Itatí, la Virgen de la Fuensanta, la Virgen de la Merced, la Virgen del Remedio y la Virgen de la Victoria. Luego proporciona pistas sobre el significado y características del nombre de
5 character classifiers
The document discusses character recognition techniques used in Tesseract. It describes how Tesseract extracts 3D feature vectors from characters and compares them to prototype vectors to classify characters. It also discusses various normalization techniques used, including character normalization to align and scale characters. The document compares Tesseract's approach to recent deep learning techniques and provides examples of how different normalizations are applied.
8 modernization efforts
Tesseract is modernizing its OCR capabilities by cleaning up code, adding newer technologies like OpenCL, and improving its classifier. OpenCL has accelerated Tesseract's processing pipeline by 36% so far. The plug-n-play classifier API allows new classifiers to be implemented. Tesseract is also exploring eliminating polygonal approximations and baseline normalization requirements. An LSTM integration is coming that will be fully integrated and allow 2D inputs and visualization.
Khmer ocr scientificday_itc
This document summarizes a Khmer optical character recognition (OCR) project. It discusses what OCR is and provides details about the Khmer OCR project team and their goal to develop a font and size independent Khmer OCR system. It reviews the state of existing Khmer OCR research and the components of the Khmer OCR system including preprocessing, segmentation, recognition, and postprocessing. It concludes with an overview of the current status of the project and future perspectives.
4 downloading
This document provides instructions for downloading and building Tesseract OCR on Linux and Windows systems. For Linux, it lists the dependencies that need to be installed via apt-get and provides commands to download, configure, make, and test the Tesseract source code. For Windows, it directs the user to download a GitHub project containing everything needed to build Tesseract in Visual Studio. It also provides high-level steps to download the Tesseract source code and demo files and then build and test the software on both platforms.
1 intro history
This document provides a summary of the history and development of the Tesseract OCR engine. It describes how Tesseract originated in the 1980s and lessons learned around readability of different fonts and formats. Key developments included extracting character outlines and topological features from images. The document outlines challenges around speed and testing that arose. It notes how Tesseract became open source in 2005 and has since expanded to support over 60 languages, with varying accuracy rates depending on the language.
6 char segmentation
This document summarizes Tesseract's approach to optical character recognition (OCR). It discusses Tesseract's use of segmentation, language models, and beam search to recognize text. Tesseract initially segments text but then refines the segmentation through chopping and combining as needed. It represents segmentation possibilities as a graph and integrates language models to search the graph and recognize words.
0 tutorial contents
This document provides an agenda for a Tesseract tutorial being held in France, outlining 8 sessions over the course of the day: an introduction; discussions of Tesseract's architecture, data structures, and training; demonstrations of features, character classification, character segmentation, language models, and beam search; a presentation on page layout analysis; and a closing segment on modernization efforts. Instructions are included to download the slides for the tutorial from Tesseract's Google Drive page.
3 training
This document provides an overview of the training process for Tesseract, an optical character recognition (OCR) engine. It discusses how training data is generated from realistic text images through various steps like text filtration, language modeling, and feature extraction. Key files generated include the unicharset containing recognizable characters, language models like DAWGs (directed acyclic word graphs) representing word lists, and trained classifier data. The training utilizes tools like mftraining and cntraining to cluster character features and build the OCR engine models.
7 layout analysis
This document discusses Tesseract's new hybrid approach to page layout analysis, which combines bottom-up and top-down methods. Specifically, it analyzes pixels to label them, finds columns via tab stops, and then identifies cross-column and non-rectangular regions. This approach aims to incorporate knowledge of typical page layouts while remaining flexible. The document provides examples of Tesseract's page layout analysis on magazine pages with vertical text, non-rectangular images, multi-orientation Japanese text, and text overlaid on images.
Khmer ocr using gfd_seminar_day
This document describes research on developing a Khmer optical character recognition (OCR) system that is independent of font and size. It involves collaboration with another researcher and uses training data to recognize characters through preprocessing, feature extraction, and a recognition process using a Generic Fourier Descriptor. The goal is to produce a reliable OCR system for the Khmer language.
2 architecture anddatastructures
The document provides an overview of the architecture and data structures used in Tesseract, an optical character recognition engine. It describes Tesseract's pipeline architecture, though notes decisions are revisited. Key data structures include pages, blocks, rows, words and characters. It also outlines Tesseract's C++ codebase originating from C, directory structure mapping functional components, and custom list implementation using macros instead of templates. Examples are given for using the TessBaseAPI interface to recognize text from images and iterate through results.
Khmer ocr using gfd
This document presents a Khmer optical character recognition system that uses generic Fourier descriptors for feature extraction and recognition. It discusses pre-processing techniques like binarization and noise removal, segmentation using connected component analysis, and extracting generic Fourier descriptors to represent each character as a feature vector. During recognition, the system compares feature vectors of input characters to those in a database to find the closest match and output the recognized text. The goal is to build a reliable Khmer OCR system that is independent of font and size.
Famouslandmarks 1
This document provides brief descriptions of famous landmarks from around the world, including the Eiffel Tower in France, the Great Pyramids in Egypt, the Acropolis in Greece, the Statue of Liberty in the United States, the Leaning Tower of Pisa in Italy, the Sydney Opera House in Australia, Big Ben in England, Christ the Redeemer statue in Rio de Janeiro Brazil, the Brazilian Carnival in Rio, Iguazu Falls on the border of Argentina and Brazil, the São Paulo Museum of Art in Brazil, and Pelourinho in Salvador Brazil.
Khmer ocr itc
The document discusses a Khmer optical character recognition (OCR) project being conducted from 2011-2012 by researchers and a student. It aims to develop a font-independent and size-independent Khmer OCR system. The document reviews the state of the art in Khmer OCR, including previous work detecting Khmer characters with up to 92.85% accuracy. It outlines the training of Tesseract, an open source OCR engine, on Khmer fonts and character clusters. Current work on the project seeks to improve previous results and develop a graphical user interface and ability to easily add new fonts.
Más contenido relacionado
Destacado
4 downloading
This document provides instructions for downloading and building Tesseract OCR on Linux and Windows systems. For Linux, it lists the dependencies that need to be installed via apt-get and provides commands to download, configure, make, and test the Tesseract source code. For Windows, it directs the user to download a GitHub project containing everything needed to build Tesseract in Visual Studio. It also provides high-level steps to download the Tesseract source code and demo files and then build and test the software on both platforms.
1 intro history
This document provides a summary of the history and development of the Tesseract OCR engine. It describes how Tesseract originated in the 1980s and lessons learned around readability of different fonts and formats. Key developments included extracting character outlines and topological features from images. The document outlines challenges around speed and testing that arose. It notes how Tesseract became open source in 2005 and has since expanded to support over 60 languages, with varying accuracy rates depending on the language.
6 char segmentation
This document summarizes Tesseract's approach to optical character recognition (OCR). It discusses Tesseract's use of segmentation, language models, and beam search to recognize text. Tesseract initially segments text but then refines the segmentation through chopping and combining as needed. It represents segmentation possibilities as a graph and integrates language models to search the graph and recognize words.
0 tutorial contents
This document provides an agenda for a Tesseract tutorial being held in France, outlining 8 sessions over the course of the day: an introduction; discussions of Tesseract's architecture, data structures, and training; demonstrations of features, character classification, character segmentation, language models, and beam search; a presentation on page layout analysis; and a closing segment on modernization efforts. Instructions are included to download the slides for the tutorial from Tesseract's Google Drive page.
3 training
This document provides an overview of the training process for Tesseract, an optical character recognition (OCR) engine. It discusses how training data is generated from realistic text images through various steps like text filtration, language modeling, and feature extraction. Key files generated include the unicharset containing recognizable characters, language models like DAWGs (directed acyclic word graphs) representing word lists, and trained classifier data. The training utilizes tools like mftraining and cntraining to cluster character features and build the OCR engine models.
7 layout analysis
This document discusses Tesseract's new hybrid approach to page layout analysis, which combines bottom-up and top-down methods. Specifically, it analyzes pixels to label them, finds columns via tab stops, and then identifies cross-column and non-rectangular regions. This approach aims to incorporate knowledge of typical page layouts while remaining flexible. The document provides examples of Tesseract's page layout analysis on magazine pages with vertical text, non-rectangular images, multi-orientation Japanese text, and text overlaid on images.
Khmer ocr using gfd_seminar_day
This document describes research on developing a Khmer optical character recognition (OCR) system that is independent of font and size. It involves collaboration with another researcher and uses training data to recognize characters through preprocessing, feature extraction, and a recognition process using a Generic Fourier Descriptor. The goal is to produce a reliable OCR system for the Khmer language.
2 architecture anddatastructures
The document provides an overview of the architecture and data structures used in Tesseract, an optical character recognition engine. It describes Tesseract's pipeline architecture, though notes decisions are revisited. Key data structures include pages, blocks, rows, words and characters. It also outlines Tesseract's C++ codebase originating from C, directory structure mapping functional components, and custom list implementation using macros instead of templates. Examples are given for using the TessBaseAPI interface to recognize text from images and iterate through results.
Khmer ocr using gfd
This document presents a Khmer optical character recognition system that uses generic Fourier descriptors for feature extraction and recognition. It discusses pre-processing techniques like binarization and noise removal, segmentation using connected component analysis, and extracting generic Fourier descriptors to represent each character as a feature vector. During recognition, the system compares feature vectors of input characters to those in a database to find the closest match and output the recognized text. The goal is to build a reliable Khmer OCR system that is independent of font and size.
Famouslandmarks 1
This document provides brief descriptions of famous landmarks from around the world, including the Eiffel Tower in France, the Great Pyramids in Egypt, the Acropolis in Greece, the Statue of Liberty in the United States, the Leaning Tower of Pisa in Italy, the Sydney Opera House in Australia, Big Ben in England, Christ the Redeemer statue in Rio de Janeiro Brazil, the Brazilian Carnival in Rio, Iguazu Falls on the border of Argentina and Brazil, the São Paulo Museum of Art in Brazil, and Pelourinho in Salvador Brazil.
Khmer ocr itc
The document discusses a Khmer optical character recognition (OCR) project being conducted from 2011-2012 by researchers and a student. It aims to develop a font-independent and size-independent Khmer OCR system. The document reviews the state of the art in Khmer OCR, including previous work detecting Khmer characters with up to 92.85% accuracy. It outlines the training of Tesseract, an open source OCR engine, on Khmer fonts and character clusters. Current work on the project seeks to improve previous results and develop a graphical user interface and ability to easily add new fonts.
Destacado (11)
La mujer
- 1. La mujer La mujer que camina delante de su sombra. Aquella a quien precede la luz como las aves a las celebraciones del solsticio.