On the light of the remarkable audio-visual effect on modern life, and the massive use of new technologies (smartphones, tablets ...), the image has been given a great importance in the field of communication. Actually, it has become the most effective, attractive and suitable means of communication for transmitting information between different people. Of all the various parts of information that can be extracted from the image, our focus will be particularly on the text. Actually, since its detection and recognition in a natural image is a major problem in many applications, the text has drawn the attention of a great number of researchers in recent years. In this paper, we present a framework for text detection and recognition from natural images for mobile devices.
In this paper, we treat the supervised data classification, while using the fuzzy random forests that combine the hardiness of the decision trees, the power of the random selection that increases the diversity of the trees in the forest as well as the flexibility of the fuzzy logic for noise. We will be interested in the construction of a forest of fuzzy decision trees. Our system is validated on nine standard classification benchmarks from UCI repository and have the specificity to control some data, to reduce the rate of mistakes and to put in evidence more of hardiness and more of interoperability.
Text detection in natural scenes holds great importance in the field of research and still remains a challenge and an important task because of size, various fonts, line orientation, different illumination conditions, weak characters and complex backgrounds in image. The contribution of our proposed method is to filtering out complex backgrounds by combining three strategies. These are enhancing the edge candidate detection in HSV space color, then using MSER candidate detection to get different masks applied in HSV space color as well as gray color. After that, we opt for the Stroke Width Transform (SWT) and heuristic filtering. Such strategies are followed so as to maximize the capacity of zones text pixels candidates and distinguish between text boxes and the rest of the image. The non-text components are filtered by classifying the characters candidates based on Support Vector Machines (SVM) using Histogram of Oriented Gradients (HOG) features. Finally we apply boundary box localization after a stage of word grouping where false positives are eliminated by geometrical properties of text blocks. The proposed method has been evaluated on ICDAR 2013 scene text detection competition dataset and the encouraging experiments results demonstrate the robustness of our method.
This article is focused in developing an improved cluster ensemble method based cluster forests. Cluster forests (CF) is considered as a version of clustering inspired from Random Forests (RF) in the context of clustering for massive data. It aggregates intermediate Fuzzy C-Means (FCM) clustering results via spectral clustering since pseudo-clustering results are presented in the spectral space in order to classify these data sets in the multidimensional data space. One of the main advantages is the use of FCM, which allows building fuzzy membership to all partitions of the datasets due to the fuzzy logic whereas the classical algorithms as K-means permitted to build just hard partitions. In the first place, we ameliorate the CF clustering algorithm with the integration of fuzzy FCM and we compare it with other existing clustering methods. In the second place, we compare K-means and FCM clustering methods with the agglomerative hierarchical clustering (HAC) and other theory presented methods using data benchmarks from UCI repository.
In this paper, we propose a new approach to Arabic printed text analysis and recognition. This approach is based on
linguistic concepts of Arabic vocabulary. For the text, we allow to categorize the words in decomposable words (derived
from a root) and indecomposable words (not derived from a root) and to put forth morpho-syntactic characterization
hypotheses for each word. For the decomposable words, we attempt to recognize word basic morphemes: antefix, prefix,
infix, suffix, postfix and root contrary to existing approaches which are usually based on recognition of word entity by