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.
Scene text does not have only one type of feature associated with it, instead it has numerous features that may play a conclusive role in correct determination of character words. Therefore, there is a need to investigate more about the features that may be distinct in nature but helpful in learning and recognition process. One such work is presented by @cite_1 . They proposed a holistic approach for image categorization that uses pyramid matching kernels to demonstrate the performance of their proposed architecture. They evaluated their proposed technique on fifteen categories using Graz dataset @cite_1 . The Graz dataset contains highly discriminative images and their proposed method provides good accuracy on image categorization. Another dataset they evaluated on Caltech-101 wasthe one which contains @math images per category. This dataset is considered as most diverse dataset provided for research purpose. The experimental setting was depicted in their manuscripts @cite_7 @cite_19 . They trained @math images per class and the test set contains @math images per class. They reported good accuracy by using their dataset as can seen further detail in their paper.
@cite_19 @cite_1 @cite_7
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Scene images generally show the characteristics of large intra-class variety and high inter-class similarity because of complicated appearances, subtle differences, and ambiguous categorization. Hence, it is difficult to achieve satisfactory accuracy by using a single representation. For solving this issue, we present a comprehensive representation for scene recognition by fusing deep features extracted from three discriminative views, including the information of object semantics, global appearance, and contextual appearance. These views show diversity and complementarity of features. The object semantics representation of the scene image, denoted by spatial-layout-maintained object semantics features, is extracted from the output of a deep-learning-based multi-classes detector by using spatial fisher vectors, which can simultaneously encode the category and layout information of objects. A multi-direction long short-term memory-based model is built to represent contextual information of the scene image, and the activation of the fully connected layer of a convolutional neural network is used to represent the global appearance of scene image. These three kinds of deep features are then fused to draw a final conclusion for scene recognition. Extensive experiments are conducted to evaluate the proposed comprehensive representation on three benchmarks scene image database. The results show that the three deep features complement to each other strongly and are effective in improving recognition performance after fusion. The proposed method can achieve scene recognition accuracy of 89.51 on the MIT67 database, 78.93 on the SUN397 database, and 57.27 on the Places365 databases, respectively, which are better percentages than the accuracies obtained by the latest reported deep-learning-based scene recognition methods. Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character detectors requires a vast of location annotated characters, which are expensive to obtain. Actually, the existing real text datasets are mostly annotated in word or line level. To remedy this dilemma, we propose a weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training. When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The character detector acts as a key role in the pipeline of our text detection engine. It achieves the state-of-the-art performance on several challenging scene text detection benchmarks. We also demonstrate the flexibility of our pipeline by various scenarios, including deformed text detection and math expression recognition. We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds. In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images. The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR'03, ICDAR'11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks. We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally performing a graph-based segmentation of CCs into words (Word-Graph). For Text-Conv, the initial detection is based on convolutional feature maps similar to those used in Convolutional Neural Networks (CNNs), but learned using Convolutional k-means. Convolution masks defined by local and neighboring patch features are used to improve detection accuracy. The Word-Graph algorithm uses contextual information to both improve word segmentation and prune false character word detections. Different definitions for foreground (text) regions are used to train the detection stages, some based on bounding box intersection, and others on bounding box and pixel intersection. Our system obtains pixel, character, and word detection f-measures of 93.14 , 90.26 , and 86.77 respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems. This approach may work for other detection targets with homogenous color in natural scenes.