Many Text Extraction methodologies have been proposed, but none of them are suitable to be part of a real system implemented on a device with low computational resources, either because their accuracy is insufficient,...Many Text Extraction methodologies have been proposed, but none of them are suitable to be part of a real system implemented on a device with low computational resources, either because their accuracy is insufficient, or because their performance is too slow. In this sense, we propose a Text Extraction algorithm for the context of language translation of scene text images with mobile phones, which is fast and accurate at the same time. The algorithm uses very efficient computations to calculate the Principal Color Components of a previously quantized image, and decides which ones are the main foreground-background colors, after which it extracts the text in the image. We have compared our algorithm with other algorithms using commercial OCR, achieving accuracy rates more than 12% higher, and performing two times faster. Also, our methodology is more robust against common degradations, such as uneven illumination, or blurring. Thus, we developed a very attractive system to accurately separate foreground and background from scene text images, working over low computational resources devices.展开更多
Extraction of the text data present in images involves Text detection, Text localization, Text tracking, Text extraction, Text Enhancement and Text Recognition. Due to its inherent complexity, traditional text localiz...Extraction of the text data present in images involves Text detection, Text localization, Text tracking, Text extraction, Text Enhancement and Text Recognition. Due to its inherent complexity, traditional text localization algorithms in natural scenes, especially in multi-context scenes, are not implementable under low computational resources architectures such as mobile phones. In this paper, we proposed a simple method to automatically localize signboard texts within JPEG mobile phone camera images. Taking into account the information provided by the Discrete Cosine Transform (DCT) used by the JPEG compression format, we delimitate the borders of the most important?text region. This system is simple, reliable, affordable, easily implementable, and quick even?working under architectures with low computational resources.展开更多
文摘Many Text Extraction methodologies have been proposed, but none of them are suitable to be part of a real system implemented on a device with low computational resources, either because their accuracy is insufficient, or because their performance is too slow. In this sense, we propose a Text Extraction algorithm for the context of language translation of scene text images with mobile phones, which is fast and accurate at the same time. The algorithm uses very efficient computations to calculate the Principal Color Components of a previously quantized image, and decides which ones are the main foreground-background colors, after which it extracts the text in the image. We have compared our algorithm with other algorithms using commercial OCR, achieving accuracy rates more than 12% higher, and performing two times faster. Also, our methodology is more robust against common degradations, such as uneven illumination, or blurring. Thus, we developed a very attractive system to accurately separate foreground and background from scene text images, working over low computational resources devices.
文摘Extraction of the text data present in images involves Text detection, Text localization, Text tracking, Text extraction, Text Enhancement and Text Recognition. Due to its inherent complexity, traditional text localization algorithms in natural scenes, especially in multi-context scenes, are not implementable under low computational resources architectures such as mobile phones. In this paper, we proposed a simple method to automatically localize signboard texts within JPEG mobile phone camera images. Taking into account the information provided by the Discrete Cosine Transform (DCT) used by the JPEG compression format, we delimitate the borders of the most important?text region. This system is simple, reliable, affordable, easily implementable, and quick even?working under architectures with low computational resources.
