The camera captured images have various aspects to investigate. Generally, the emphasis of research depends on the interesting regions. Sometimes the focus could be on color segmentation, object detection or scene text analysis. The image analysis, visibility and layout analysis are the tasks easier for humans as suggested by behavioral trait of humans, but in contrast when these same tasks are supposed to perform by machines then it seems to be challenging. The learning machines always learn from the properties associated to provided samples. The numerous approaches are designed in recent years for scene text extraction and recognition and the efforts are underway to improve the accuracy. The convolutional approach provided reasonable results on non-cursive text analysis appeared in natural images. The work presented in this manuscript exploited the strength of linear pyramids by considering each pyramid as a feature of the provided sample. Each pyramid image process through various empirically selected kernels. The performance was investigated by considering Arabic text on each image pyramid of EASTR-42k dataset. The error rate of 0.17 was reported on Arabic scene text recognition.
The spatial pyramid model presents very interesting results by incorporating other techniques for a purpose to enhance the performance. One such technique is presented by @cite_8 , they proposed two different models with incorporation of spatial pyramid knowledge. Their first model is an image independent model while the second one adapted the image contents. They used PASCAL VOC 2007, 2008, and 2009 dataset for evaluation of their proposed models. Their average results vary from @math Although application of spatial linear pyramid is new for scene text recognition problems. SPM has not been applied before for categorization of text related problems. This paper is presenting a novel solution for text localization problem. As this technique has been comparatively applied successfully, on number of object recognition in scene images as can witnessed in @cite_1 @cite_7 @cite_8 @cite_17 . The presented work is exploiting its potential in the field of text localization.
@cite_1 @cite_17 @cite_7 @cite_8
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This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralbas "gist" and Lowes SIFT descriptors. Multi-scale representations are motivated by the scale invariant properties of natural images. While many low level statistical measures, such as the local mean and variance of intensity, behave in a scale invariant manner, there are many higher order deviations from scale invariance where zero-crossings merge and disappear. Such scale variant behavior is important information to represent because it is not easily predicted from lower resolution data. A scale variant image pyramid is a representation that separates this information from the more redundant and predictable scale invariant information. Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondences epsivnerally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. This "pyramid match" computation is linear in the number of features, and it implicitly finds correspondences based on the finest resolution histogram cell where a matched pair first appears. Since the kernel does not penalize the presence of extra features, it is robust to clutter. We show the kernel function is positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels. We demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches Several state-of-the-art image representations consist in averaging local statistics computed from patch-level descriptors. It has been shown by that such average statistics suffer from two sources of variance. The first one comes from the fact that a finite set of local statistics are averaged. The second one is due to the variation in the proportion of object-dependent information between different images of the same class. For the problem of object classification, these sources of variance affect negatively the accuracy since they increase the overlap between class-conditional probabilities. Our goal is to include information about the spatial layout of images in image signatures based on average statistics. We show that the traditional approach to including the spatial layout - the spatial pyramid (SP) - increases the first source of variance while only weakly reducing the second one. We therefore propose two complementary approaches to account for the spatial layout which are compatible with our goal of variance reduction. The first one models the spatial layout in an image-independent manner (as is the case of the SP) while the second one adapts to the image content. A significant benefit of these approaches with respect to the SP is that they do not incur an increase of the image signature dimensionality. We show on PASCAL VOC 2007, 2008 and 2009 the benefits of our approach.