In this paper, we propose an effective approach for a PDA-based sign system, and it presents user the sign translator. Its main functions include 3 parts: detection, recognition and translation. Automatic detection and recognition of text in natural scenes is a prerequisite for automatic sign translator. In order to make the system robust for text detection in various natural scenes, the detection approach efficiently embeds multi-resolution, adaptive search in a hierarchical framework with different emphases at each layer. We also introduce an intensity-based OCR method to recognize character in various fonts and lighting condition, where we employ Gabor transform to obtain local features, and LDA for selection and classification of features. The recognition rate is 92.4% for the testing set got from the natural sign. Sign is different from the normal used sentence. It is brief, with a lot of abbreviations and place nouns. We here only briefly introduce a rule-based place name translation. We have integrated all these functions in a PDA, which can capture sign image, auto segment and recognize the Chinese sign, and translate it into English.

A sign is something that suggests the presence of a fact, condition, or quality. Signs are everywhere in our lives. They make our lives easier when we are familiar with them. But sometimes they pose problems. For example, a tourist might not be able to understand signs in a foreign country. This paper discusses problems of automatic sign recognition and translation. We present a system capable of capturing images, detecting and recognizing signs, and translating them into a target language. We describe methods for automatic sign extraction and translation. We use a user-centered approach in system development. The approach takes advantage of human intelligence if needed and leverage human capabilities. We are currently working on Chinese sign translation. We have developed a prototype system that can recognize Chinese sign input from a video camera that is a common gadget for a tourist, and translate the signs into English or voice stream. The sign translation, in conjunction with spoken language translation, can help international tourists to overcome language barriers. The technology can also help a visually handicapped person to increase environmental awareness.

In this paper, we propose a robust approach for recognition of text embedded in natural scenes. Instead of using binary information as most other OCR systems do, we extract features from intensity of an image directly. We utilize a local intensity normalization method to effectively handle lighting variations. We then employ Gabor transform to obtain local features, and use LDA for selection and classification of features. The proposed method has been applied to a Chinese sign recognition task. The system can recognize a vocabulary of 3755 Level 1 Chinese characters in the Chinese national standard character set GB2312-80 with various print fonts. We tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy is 92.46%. We have already integrated the system into our automatic Chinese sign translation system. 1.

In this paper, we propose an approach for detecting signs from natural scenes. The approach efficiently embeds multiresolution, adaptive search, and affine rectification algorithms in a hierarchical framework, with different emphases at each layer. We combine multi-resolution and multi-scale edge detection techniques to effectively detect text in different sizes. Different from the existing approaches, by using the cues from text inside the image, we introduce affine rectification transformation to recover deformation of the text region caused by an inappropriate camera view angle. This procedure can significantly improve text detection rate and OCR (Optical Character Recognition) accuracy. Experimental results have demonstrated feasibility of the proposed algorithms. We have applied the proposed approach to a Chinese sign translation system, which can

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A sign is something that suggests the presence of a fact, condition, or quality. Signs are everywhere in our lives. They make our lives easier when we are familiar with them. But sometimes they pose problems. For example, a tourist might not be able to understand signs in a foreign country. This paper discusses problems of automatic sign recognition and translation. We present a system capable of capturing images, detecting and recognizing signs, and translating them into a target language. We describe methods for automatic sign extraction and translation. We use a user-centered approach in system development. The approach takes advantage of human intelligence if needed and leverage human capabilities. We are currently working on Chinese sign translation. We have developed a prototype system that can recognize Chinese sign input from a video camera that is a common gadget for a tourist, and translate the signs into English or voice stream. The sign translation, in conjunction with spoken language translation, can help international tourists to overcome language barriers. The technology can also help a visually handicapped person to increase environmental awareness.

In this paper, we present a novel approach to accurately detect text regions including shop name in signboard images with complex background for mobile system applications. The proposed method is based on the combination of text detection using edge profile and region segmentation using fuzzy c-means method. In the first step, we perform an elaborate canny edge operator to extract all possible object edges. Then, edge profile analysis with vertical and horizontal direction is performed on these edge pixels to detect potential text region existing shop name in a signboard. The edge profile and geometrical characteristics of each object contour are carefully examined to construct candidate text regions and classify the main text region from background. Finally, the fuzzy c-means algorithm is performed to segment and detected binarize text region. Experimental results show that our proposed method is robust in text detection with respect to different character size and color and can provide reliable text binarization result.

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Text reading in images or video is important step to achieve content retrieval from images. The content retrieve from image or video content useful information it act as clue for many image based applications such as scene understanding, content based image retrieval text based image indexing, industrial automation. Locating or detecting text from complex background is a challenging task due to variation in size, orientation, style. This paper describes the techniques which try to solving this problem.