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Report comment. Hi, I was trying to bypass the 40 seconds in megaupload. And THX. Megaupload just that enrages! Better all still use rapidshare …. Leave a Reply Cancel reply. Search Search for:. Hackaday Links: January 9, 10 Comments. Loading Comments Email Required Name Required Website. When comparing features of different characters, the various transformations may reduce recognition accuracy.

If the images meet these conditions, the noise is difficult to remove. This may reduce recognition accuracy. According to whether there is segmentation or not, the existing breaking methods be contained in two categories. When there is no adherent character, individual characters are obtained using vertical projection and connected component with good effect.

However, it had little success in adherent characters. Therefore, more complicated methods, such as different width, character features, and character contours, have been proposed one after another. Then the researchers proposed the segmentation methods for obtaining character components by character structure, filters, and so forth. In early stage, different pattern matching algorithms such as shape context [ 20 ] and similarity [ 57 ] are used for recognition.

Later, with the improvement of the success rates of individual character recognition, researchers focus on the character segmentation technique. Nowadays with the advantage of deep learning, the breaking based on nonsegmentation will bounce back. The framework mainly consists of preprocessing, segmentation, combination, recognition, postprocessing, and other modules. The research progress of each module will be described in the following.

Its main purpose is to highlight the information related to characters in a given image and to weaken or eliminate interfering information. The key to binarization is to select an appropriate threshold. When the threshold is applied to the whole image, it is called the global threshold method; otherwise, it is called the local threshold method. If the threshold is not fixed during processing, it is called variable threshold method or dynamic threshold method.

Its purpose is to highlight image contour and to simplify subsequent processing. The thinning algorithms contain two categories: noniterative algorithm and iterative algorithm. The common thinning algorithms include Hilditch algorithm [ 67 ] and Zhang and Suen algorithm [ 68 ]. In addition, some noises are generated during grayscale and binarization. The typical methods are as shown in Table 5. We should choose the effective denoising method according to actual situation. The segmentation aims to get individual characters or character components.

There are the segmentation methods based on individual characters and the segmentation methods based on character components. For individual characters, we can use segmentation methods based on character projection and connected components. For CCT characters, we can use segmentation methods based on character width, connected feature, and character contour. The segmentation methods based on character projection determine the optimal segmentation position by analyzing the number of pixels projected under different conditions.

However, its effect is not obvious for the seriously adherent and distorted characters. The typical methods include vertical projection segmentation, horizontal projection segmentation, and guideline projection segmentation. In three-color bar a column is colored in blue if there is not any pixel that belongs to character in the column. If there is only one pixel in column , the column is encoded by white. Finally, the black corresponds to the column with more than one object pixel , as shown in Figure 2 a.

After denoising, the optimal segmentation line is determined in the middle of blue bar or white bar, as shown in Figure 2 b. The segmentation methods based on connected components effectively segment individual characters using different connected components in an image.

For slope and distortion characters, this method is effective. However, it is limited by adherent characters. First, different connected components are marked with different colors. And then the character blocks are generated according to different colors. Thus, each character corresponds to four recognition results, from which to find an optimal segment as the final recognition result. In addition, [ 5 ] did not take the average width as standard; they gave a set of character segments between the minimum width and the maximum width and then determined the optimal segmentation scheme using dynamic programming, as shown in Figure 4.

Reference [ 38 ] classifies characters according to their own inside features, and each class contains the characters as shown in Table 6. Reference [ 6 ] segments characters according to outside features among them. The algorithm utilizes the central pixel in background between two disconnected object pixels as segmentation points see Figure 5. The segmentation method based on character contours is to analyze geometric features of character contours, so as to determine the appropriate segmentation lines.

Reference [ 7 ] tried to connect connection edge points between two merged characters and determined the optimal segmentation line by confidence, as shown in Figure 6. The segmentation methods based on character components produce multiple character components, rather than individual characters.

The segmentation methods are mainly base on character structure or filter. Using structural feature of characters with black components and white components, [ 36 ] segmented a seriously overlapped string to multiple components. First, locate black components, as shown in Figure 7 b. And then, locate white components, as shown in Figure 7 c. Finally, identify black components of each character and the shared white components.

According to this structural feature, a character is segmented to several character components by color filling see Figure 8 b. The segmentation method is not limited by adhesion, distortion, and overlap and is suitable for many kinds of characters.

In summary, the contrast among segmentation methods is given. As can be seen in Table 7 , each segmentation method applies to different types of characters. It is only the individualized segmentation method that can obtain good results. An individual character after segmentation can be recognized directly. But character components need to be combined into an individual character to be recognized. According to the number of generated candidate characters, combination technologies can be divided into two categories: the combination technique based on redundancy and the combination technique based on nonredundancy.

The number of candidate characters generated by combination technique based on redundancy is more than the number of real characters. In [ 42 ], each character fragment is labeled in order from top to bottom and left to right, and then the components are combined on the idea of jigsaw puzzle to generate candidate characters.

The number of candidate characters generated by combination technique based on nonredundancy is equal to the number of actual characters. In [ 36 ], the character components are nonredundant. The overlap area strokes may be reused to compose a complete character. Figure 10 gives the combined four characters. The final success rate of combination is Template matching is to compare similarity of each pixel between characters and every template and to find the highest similarity.

According to matching range, there are the matching recognition methods based on global property and the matching recognition methods based on local feature. The matching recognition methods based on global property is traverse scanning. Within search area, the optimal match point to each pixel is found by regional correlation matching calculation.

Because many templates matching each pixel will be pretty slow, [ 45 ] proposes the second template matching algorithm to improve efficiency. Only if a rough matching is successful, an exact matching needs to be made. The shape context is a simple local feature shape descriptor.

Its basic idea is to convert the matching problem of image into the matching problem of feature point set. The structural feature can describe the details and structural information of characters, such as the number of loops, inflection point, convexo-concave degree, and cross points. The recognition method based on character statistical feature uses commonly statistical features including pixel feature, projection feature, contour feature, and coarse mesh feature.

According to chronological order of mainstream, it can be basically divided into three categories: traditional methods, neural network, and deep learning. The idea of SVM is to separate classes via a hyperplane. The key is kernel function, which is responsible for mapping original features into high-dimensional space in a nonlinear way, thereby improving the separability for data.

The experimental results showed that the performance of the first two kernel functions was optimal. KNN is based on the category of the nearest samples to determine the category of a sample. Among these classifiers, KNN achieved higher success rates on most of the schemes, but CNN was faster most of the time. For the principle of parallel distributed operation in large number of neurons, the efficient learning algorithms, and the ability to imitate human cognitive systems, the neural network is very suitable to solve problems such as speech recognition and text recognition.

In [ 62 ], a BPNN used cross entropy for calculating the performance of a network with targets and outputs. Eventually, the system achieved an overall precision of However, when applying neural network, we need to extract character features first. The quality of extracted features limits the final recognition rate to a certain extent.

In recent years, deep learning has achieved remarkable achievements in recognition fields of text, image, audio, and so forth. CNN recognizes character images without feature extraction and has a certain degree of robustness in displacement, scale, and deformation.

In the existing research results, a typical CNN is widely used [ 2 , 4 , 36 , 38 , 41 ] with a good recognition accuracy. Reference [ 30 ] trained large, distributed deep convolutional neural networks and achieved However, due to lack of time dimension, CNN cannot combine context information in recognition.

So RNN with feedback and time parameters was proposed to process time series data. It innovatively obtained relative information not only in horizontal context, but also in vertical context.

In summary, a contrast among recognition methods is given, as shown in Table 8. According to the features of different networks, we should attempt to construct a new deep learning model by combining multiple networks. In previous steps, some of character recognition results may be taken as final results directly, while others need to be further postprocessed. According to different objects and methods, there are the postprocessing methods based on selection and the postprocessing methods based on rejection.

Usually, there are many redundant individual characters generated in previous steps. The selection strategies include the local optimization and the global optimization. The local optimization selection only takes into account the recognition confidence optimality of an individual character.

In [ 60 ], each character corresponds to several candidate characters with different widths. Therefore, the candidate character with the highest recognition confidence is selected as the final character.

The global optimization selection strives for the best results for all characters in an image. Compared with graph traversal, the dynamic programming is more effective and accurate. The purpose of postprocessing methods based on rejection is to determine whether the tested sample belongs to the types of training set by analyzing character recognition results.

At present, the researchers have not been paid enough attention to the postprocessing methods based on rejection. It considers multiple features, such as confidence, string length, character spaces, and the first and the last character of a string, to determine whether a candidate character should be rejected or not.

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