Text segmentation in mixed-mode images

Chaddha, N.; Sharma, Rahul; Agrawal, Akshay; Gupta, Agrim

doi:10.1109/acssc.1994.471679

Cited by 24 publications

(9 citation statements)

References 4 publications

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“…The results from the individual methods are then merged to produce the ÿnal bounding boxes. They utilize algorithms by Gargi et al [29,32] (see Section 2.2.3), Chaddha et al [57], Schaar-Mitrea and de With [58], and LeBourgeois [59]. Chaddha et al use texture energy to classify 8 × 8 pixel blocks into text or non-text blocks.…”

Section: Other Approachesmentioning

confidence: 98%

“…Antani et al and Hua et al recently presented their comparison methodologies and results. Antani et al [22] used a formal evaluation method for TIE, and selected ÿve algorithms [29,[57][58][59]71] as promising. Each algorithm was slightly modiÿed for the sake of comparison and the ground truth data was generated, frame-by-frame, manually to include text box size, position, and orientation angle.…”

Section: Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Text information extraction in images and video: a survey

Jung¹,

Kim²,

Jain³

2004

Pattern Recognition

719

343

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Section: Other Approachesmentioning

confidence: 98%

Section: Reviewmentioning

confidence: 99%

Text information extraction in images and video: a survey

Jung¹,

Kim²,

Jain³

2004

Pattern Recognition

719

343

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“…DCT Coefficients. The DCT based descriptors has shown to be one of the most accurate, compact, and robust methods for the extraction of local texture features in the spatial frequency domain (Chaddha, 1994). The DCT descriptor was herein obtained by applying the DCT to each stain component image and storing the 21 lower frequency coefficients.…”

Section: Representing Images As a Bag Of Visualmentioning

confidence: 99%

Micro‐structural tissue analysis for automatic histopathological image annotation

Díaz

Romero

2011

Microscopy Res & Technique

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This article presents a new approach for extracting high level semantic concepts from digital histopathological images. This strategy provides not only annotation of several biological concepts, but also a coarse location of these concepts. The proposed approach is composed of five main steps: (1) a stain decomposition stage, which separates the contribution of hematoxylin and eosin dyes, (2) a color standardization that corrects color image differences, (3) a part-based representation, which describes the image in terms of the conditional probability of relevant local patches, selected by their stain contributions, (4) a discriminative classification model, which bridges out the found patterns and the biological concepts, (5) a block-based annotation strategy that identifies the multiple biological concepts within an image. A set of 655 skin images, containing 10 biological concepts of skin tissues were used for assessing the proposed approach, obtaining a sensitivity of 84% and a specificity of 67% when annotating images with multiple concepts.

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“…For fast and robust edge map transformation, DCT has been employed by many researchers. Chaddha et al [6] studied lists of DCT coefficient used for best coefficient search and proposed a list of eight combinations. Zhong et al [7] defined DCT energy functions using horizontal and vertical spatial intensity.…”

Section: Related Workmentioning

confidence: 99%

Text Region Extraction and OCR on Camera Based Images

Shin¹

2010

The KIPS Transactions:PartD

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Traditional OCR engines are designed to the scanned documents in calibrated environment. Three dimensional perspective distortion and smooth distortion in images are critical problems caused by un-calibrated devices, e.g. image from smart phones. To meetthe growing demand of character recognition of texts embedded in the photos acquired from the non-calibrated hand-held devices, we address the problem in three categorical aspects: rotational invariant method of text region extraction, scale invariant method of text line segmentation, and three dimensional perspective mapping. With the integration of the methods, we developed an OCR for camera-captured images.

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Text segmentation in mixed-mode images

Cited by 24 publications

References 4 publications

Text information extraction in images and video: a survey

Text information extraction in images and video: a survey

Micro‐structural tissue analysis for automatic histopathological image annotation

Text Region Extraction and OCR on Camera Based Images

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