Categorizing Document Images into Script and Language Classes

Suen, Ching Y.; Bergler, Sabine; Nobile, Nicola; Waked, B.; Nadal, C.; Bloch, Alexia

doi:10.1007/978-1-4471-0833-7_30

Cited by 12 publications

(10 citation statements)

References 4 publications

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“…However, all sample images in corpus three are coupled with slight skew distortion with skew angle controlled under 20 degree. Unlike some methods [7,8] that require document restoration first, word images are transformed to WSCs directly based on our proposed word shape coding scheme. Experiment results show 157 text image are correctly identified with average identification rate reaching over 97%.…”

Section: Language Identificationmentioning

confidence: 99%

“…Lastly, most reported language identification techniques [5,7,8] cannot identify language in document images that contain just a few words. We therefore construct the fourth corpus to evaluate the performance of our proposed technique with respect to word number.…”

Section: Language Identificationmentioning

confidence: 99%

“…Character coding is normally implemented based on the character shape characteristics including character ascender and descender and character ascent and decent. For example, the works in [5,7,8] propose to first group character and other text symbol images into six, ten, and thirteen categories, respectively. With the character categorization results, a set of word shape codes (WSCs) are then created based on the word segmentation results.…”

Section: Introductionmentioning

confidence: 99%

“…With the character categorization results, a set of word shape codes (WSCs) are then created based on the word segmentation results. Finally, languages are differentiated according to the WSC frequency profiles of a single word [5,7], word pair, and word trigram [8]. Linear discriminate analysis (LDA) [5] and rule based systems [7,8] are exploited for the final language identification.…”

Section: Introductionmentioning

confidence: 99%

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Language Identification in Degraded and Distorted Document Images

Tan

Huang

2006

Document Analysis Systems VII

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Abstract. This paper presents a language identification technique that differentiates Latin-based languages in degraded and distorted document images. Different from the reported methods that transform word images through a character shape coding process, our method directly captures word shapes with the local extremum points and the horizontal intersection numbers, which are both tolerant of noise, character segmentation errors, and slight skew distortions. For each language studied, a word shape template and a word frequency template are firstly constructed based on the proposed word shape coding scheme. Identification is then accomplished based on Bray Curtis or Hamming distance between the word shape code of query images and the constructed word shape and frequency templates. Experiments show the average identification rate upon eight Latin-based languages reaches over 99%. . . .

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Section: Language Identificationmentioning

confidence: 99%

Section: Language Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Language Identification in Degraded and Distorted Document Images

Tan

Huang

2006

Document Analysis Systems VII

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“…A number uses character-based features or connected component analysis [2], [3]. The paradox inherent in such an approach is that it is sometimes necessary to know the script of the document in order to extract such components.…”

Section: Introductionmentioning

confidence: 99%

Texture for script identification

Busch

Boles

Sridharan

2005

IEEE Trans. Pattern Anal. Machine Intell.

154

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Abstract-The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.

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Learning Visual Shape Lexicon for Document Image Content Recognition

Zhu

et al. 2008

Lecture Notes in Computer Science

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Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4, 500 Web images crawled from Google Image Search into three content categories-pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1, 512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.

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Categorizing Document Images into Script and Language Classes

Cited by 12 publications

References 4 publications

Language Identification in Degraded and Distorted Document Images

Language Identification in Degraded and Distorted Document Images

Texture for script identification

Learning Visual Shape Lexicon for Document Image Content Recognition

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