To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
|
|
Title |
An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image |
|
Author |
G. Lalitha and B. Lavanya |
| Citation |
Vol. 22 No. 7 pp. 220-228 |
|
Abstract |
Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding’s plays a vital role in road safety measures. At initial stage applying pre-processing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies. |
|
Keywords |
MSER ? Maximally Stable Extremal Region, OCR ? Optical Character Recognition, CC ? Connected Components, SWV- Stroke Width Variation. |
|
URL |
