25,388 research outputs found
Text Extraction from Web Images Based on A Split-and-Merge Segmentation Method Using Color Perception
This paper describes a complete approach to the segmentation and extraction of text from Web images for subsequent recognition, to ultimately achieve both effective indexing and presentation by non-visual means (e.g., audio). The method described here (the first in the authorsâ systematic approach to exploit human colour perception) enables the extraction of text in complex situations such as in the presence of varying colour (characters and background). More precisely, in addition to using structural features, the segmentation follows a split-and-merge strategy based on the Hue-Lightness- Saturation (HLS) representation of colour as a first approximation of an anthropocentric expression of the differences in chromaticity and lightness. Character-like components are then extracted as forming textlines in a number of orientations and along curves
Query by String word spotting based on character bi-gram indexing
In this paper we propose a segmentation-free query by string word spotting
method. Both the documents and query strings are encoded using a recently
proposed word representa- tion that projects images and strings into a common
atribute space based on a pyramidal histogram of characters(PHOC). These
attribute models are learned using linear SVMs over the Fisher Vector
representation of the images along with the PHOC labels of the corresponding
strings. In order to search through the whole page, document regions are
indexed per character bi- gram using a similar attribute representation. On top
of that, we propose an integral image representation of the document using a
simplified version of the attribute model for efficient computation. Finally we
introduce a re-ranking step in order to boost retrieval performance. We show
state-of-the-art results for segmentation-free query by string word spotting in
single-writer and multi-writer standard datasetsComment: To be published in ICDAR201
Logical segmentation for article extraction in digitized old newspapers
Newspapers are documents made of news item and informative articles. They are
not meant to be red iteratively: the reader can pick his items in any order he
fancies. Ignoring this structural property, most digitized newspaper archives
only offer access by issue or at best by page to their content. We have built a
digitization workflow that automatically extracts newspaper articles from
images, which allows indexing and retrieval of information at the article
level. Our back-end system extracts the logical structure of the page to
produce the informative units: the articles. Each image is labelled at the
pixel level, through a machine learning based method, then the page logical
structure is constructed up from there by the detection of structuring entities
such as horizontal and vertical separators, titles and text lines. This logical
structure is stored in a METS wrapper associated to the ALTO file produced by
the system including the OCRed text. Our front-end system provides a web high
definition visualisation of images, textual indexing and retrieval facilities,
searching and reading at the article level. Articles transcriptions can be
collaboratively corrected, which as a consequence allows for better indexing.
We are currently testing our system on the archives of the Journal de Rouen,
one of France eldest local newspaper. These 250 years of publication amount to
300 000 pages of very variable image quality and layout complexity. Test year
1808 can be consulted at plair.univ-rouen.fr.Comment: ACM Document Engineering, France (2012
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Recognition by directed attention to recursively partitioned images
A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth
Two Approaches for Text Segmentation in Web Images
There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characters with an anthropocentric perspective of colour perceptionâ in preference to RGB space analysis. Both approaches enable the extraction of text in complex situations such as in the presence of varying colour and texture (characters and background)
Two Approaches for Text Segmentation in Web Images
There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characters with an anthropocentric perspective of colour perceptionâ in preference to RGB space analysis. Both approaches enable the extraction of text in complex situations such as in the presence of varying colour and texture (characters and background)
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