CHARACTER DESIGN FOR MOBILE DEVICES PDF

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Character Design For Mobile Devices Pdf

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At first, the captured image is skew corrected by looking for a line having the highest number Segmentation of consecutive white pixels and by maximizing the given alignment criterion. Then, the image is segmented based Recognition on X-Y Tree decomposition and recognized by measuring Manhattan distance based similarity for a set of centroid to boundary features.

However, this work addresses only the English capital letters and the accuracy obtained is not Text satisfactory for real life applications. Block diagram of the present system Luo et al. In [8], a b usiness card image is first 2. Present Work down sampled to estimate the skew angle. Then the text regions are skew corrected by that angle and binarized Modern day handheld devices are usually capable of thereafter. Such text regions are segmented into lines and capturing color images.

A color image consists of color characters, and subsequently passed to an OCR engine for pixels represented by a combination of three basic color recognition. The OCR engine is designed as a two layer components viz. The range template based classifier. A similar system is presented for of values for all these color components is So, the Chinese-English mixed script business card images in [9].

Shen at el. But, of course, the algorithms deployed in these the image. Such a gray level image is fed as input to the systems must be computation friendly.

They should be proposed character recognition system. The block diagram of the present character recognition Under the current work, a character recognition system is system is shown in Fig.

The such as business card images.

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NRs are removed and the TRs are de-skewed as discussed in Sec 2. In Sec. Finally, the characters are recognized as illustrated in Sec. These values are the distances in terms of width of the block respectively. Among these four profiles, the one which is from Block BB based on the intensity variation within it. The area of a region is always a multiple of the area of the blocks. Calculation of skew angle from bottom profile characteristics features of textual and non-textual regions of a text region such as dimensions, aspect ratio, information pixel density, region area, coverage ratio, histogram, etc.

Then, the and the TRs extracted from it. The central idea behind this exclusion is that these elements hardly contribute to the actual skew of the text region. Now, from the remaining profile elements, we choose the leftmost h1, right-most h2 and the middle one h3. The final skew angle is computed by averaging the three skew angles obtained from the three pairs h1-h3, h3-h2 and h1-h2.

Once the skew angle for a text region is estimated, it is rotated by the same.

A camera captured image and the text regions extracted from it 2. The algorithm has been given below. In his method, the arithmetic capturing the image. The acquired image does not become mean of the maximum G max and the minimum G min uniformly skewed mainly due to perspective distortion. Therefore, the image cannot be de-skewed at a binarization are also taken as deciding factors for single pass. On the other hand, the effect of perspective binarization. This type of approach is especially useful to distortion is distributed throughout the image.

Its effect is connect the disconnected foreground pixels of a character. At the same time, we see that the image segmentation module generates only a few text regions.

So, these text regions are de-skewed using a computationally efficient and fast skew correction technique designed in our work and published in [15]. A brief description has been given here. Every text region has two types of pixels — dark and gray. The dark pixels constitute the texts and the gray pixels are background around the texts.

Here 2. At Fig. After resizing the pattern by thresholding the profile values. The threshold is chosen so its bounding box, it is normalized to a standard dimension, as to allow over-segmentation. Text line boundaries are 48x Thus, n such segments represent n-1 text lines. After that the inter-segment distances are analyzed and some segments are rejected based on the idea that the distance between two lines in terms of pixels will Boundary Selection not be too small and the inter-segment distances are likely to become equal.

A detail description of the method is Normalization given in [18]. Using vertical histogram profile of each individual text lines, words and characters are segmented. Sample segmented characters have been shown in Fig. Use of horizontal histogram of text regions for their document images.

And among these 94 characters, only 73 segmentation are frequently used. In the present scope of experiment, we a A skewed text region and its horizontal histogram, b Skew corrected text region and its horizontal histogram have considered 73 classes recognition problem. These 73 characters are listed in Table 1. These include 26 capital letters, 26 small letters, 10 numeric digits and 11 special characters. The images have been captured with a n o p q r s t u v w cell phone Sony Ericsson Ki camera.

Experiments reveal that the classification ground truth and output images, it is classified as true accuracy gets considerably reduced because of positive TP , false positive FP , true negative TN and misclassification among these similar classes.

Therefore, false negative FN. So, F-Measure FM can be defined in each such group of similar classes in the 73 class terms of recall rate R and precision rate P can be given recognition problem will be considered as a single class.

In as Eq. List of Symmetric Classes FN denote their respective counts. In an ideal situation i. Segmented text regions are then skew Z Capital , z Small Z corrected and binarized. In the present scope of experiments, both 73 class and 62 class recognition problems have been experimented and the Although binarization is done after skew correction in the results obtained have been reported.

To quantify the binarization accuracy, 10 representative samples from each class have been taken similar method as discussed above has been adopted. More Pixel-wise ground truth images are compared with that of samples could have been taken, but that would increase the the output binarized images.

From these counts, we get an average present work. Experiments show that 10 is a good choice.

Now, the segmented into text lines. These values are the distances in terms of width of the block respectively. Among these four profiles, the one which is from Block BB based on the intensity variation within it.

The area of a region is always a multiple of the area of the blocks. Calculation of skew angle from bottom profile characteristics features of textual and non-textual regions of a text region such as dimensions, aspect ratio, information pixel density, region area, coverage ratio, histogram, etc.

Then, the and the TRs extracted from it. The central idea behind this exclusion is that these elements hardly contribute to the actual skew of the text region.

Now, from the remaining profile elements, we choose the leftmost h1, right-most h2 and the middle one h3. The final skew angle is computed by averaging the three skew angles obtained from the three pairs h1-h3, h3-h2 and h1-h2. Once the skew angle for a text region is estimated, it is rotated by the same.

A camera captured image and the text regions extracted from it 2. The algorithm has been given below. In his method, the arithmetic capturing the image. The acquired image does not become mean of the maximum G max and the minimum G min uniformly skewed mainly due to perspective distortion.

Therefore, the image cannot be de-skewed at a binarization are also taken as deciding factors for single pass. On the other hand, the effect of perspective binarization. This type of approach is especially useful to distortion is distributed throughout the image. Its effect is connect the disconnected foreground pixels of a character.

At the same time, we see that the image segmentation module generates only a few text regions. So, these text regions are de-skewed using a computationally efficient and fast skew correction technique designed in our work and published in [15]. A brief description has been given here.

Every text region has two types of pixels — dark and gray. The dark pixels constitute the texts and the gray pixels are background around the texts. Here 2. At Fig. After resizing the pattern by thresholding the profile values. The threshold is chosen so its bounding box, it is normalized to a standard dimension, as to allow over-segmentation.

Text line boundaries are 48x Thus, n such segments represent n-1 text lines. After that the inter-segment distances are analyzed and some segments are rejected based on the idea that the distance between two lines in terms of pixels will Boundary Selection not be too small and the inter-segment distances are likely to become equal.

A detail description of the method is Normalization given in [18]. Using vertical histogram profile of each individual text lines, words and characters are segmented. Sample segmented characters have been shown in Fig.

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Use of horizontal histogram of text regions for their document images. And among these 94 characters, only 73 segmentation are frequently used. In the present scope of experiment, we a A skewed text region and its horizontal histogram, b Skew corrected text region and its horizontal histogram have considered 73 classes recognition problem. These 73 characters are listed in Table 1.

These include 26 capital letters, 26 small letters, 10 numeric digits and 11 special characters. The images have been captured with a n o p q r s t u v w cell phone Sony Ericsson Ki camera.

Mobile device

Experiments reveal that the classification ground truth and output images, it is classified as true accuracy gets considerably reduced because of positive TP , false positive FP , true negative TN and misclassification among these similar classes.

Therefore, false negative FN. So, F-Measure FM can be defined in each such group of similar classes in the 73 class terms of recall rate R and precision rate P can be given recognition problem will be considered as a single class. In as Eq. In an ideal situation i. Segmented text regions are then skew Z Capital , z Small Z corrected and binarized. In the present scope of experiments, both 73 class and 62 class recognition problems have been experimented and the Although binarization is done after skew correction in the results obtained have been reported.

To quantify the binarization accuracy, 10 representative samples from each class have been taken similar method as discussed above has been adopted. More Pixel-wise ground truth images are compared with that of samples could have been taken, but that would increase the the output binarized images.

From these counts, we get an average present work. Experiments show that 10 is a good choice. Now, the segmented into text lines. Besides having skew corrected printed texts, soundness of the segmentation method makes text region segmentation so accurate. It may be noted that the present segmentation technique is not meant for italic and cursive texts. So, such Authors are thankful to the Center for Microprocessor texts have been ignored while calculating the segmentation Application for Training Education and Research accuracy.

The Department of Computer Science and Engineering, maximum recognition accuracy has been achieved with 62 Jadavpur University for providing infrastructural support class recognition problem.

A close observation reveals that for the research work. The second author is thankful to MCKV Institute of Table 3: Percentage of recognition with different classifiers Engineering for allowing her in carrying out research.

Dunlop and S. International Conference on Pattern Recognition, , pp.

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Because of the computing constraints of handheld devices, [10] Masashi Koga, Ryuji Mine, Tatsuya Kameyama, Toshikazu we have kept our study limited to light-weight and Takahashi, Masahiro Yamazaki and Teruyuki Yamaguchi, computationally efficient techniques. Experiments shows that the recognition system on Document Analysis and Recognition, , pp. ICDAR, , pp.

Implementing the [13] K. Bae, K. Kim, Y. Chung and W. Computer Software and Applications Conference, vol. Mollah, S.Easily bring your character to life through your own performance. Haptic button is another approach to tackle the challenge.

Only a part of the screen would be a genuinely effortless territory for their thumbs. Input usability assessment studies for children users were rarer.

Das, R. Users are encouraged to jump in and do a quick test in their selected language.