# A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
## 2 Related Work
Local features, in general, involve very precise and convolutional operations applied over the logo contour, which makes this kind of method very coupled with a particular environment. As another example of these hybrid approaches, [PERSON]. [16] used curvature and the distance to the centroid as local features and Zernike moments as global features. Then, similarities are computed using the Euclidean distance. [PERSON]. [17] described logos using a variant of shape context descriptors and indexing over a database. [PERSON]. [18] used several kinds of descriptors (Fourier transform, chain coded strings, etc.) to index and match over a trademark logotype database. As before, this approach did not perform well when occluded logos were present. [PERSON]. [19] presents a logo-spotting framework applied to spotting logo images in document images and focused on the document-categorization and document-retrieval problems. This method is focused in terms of searching for matches between all interest points of the query logo images and of document images. From the images, the interest points are extracted. The local feature vectors are described by local detectors and local descriptors. The experiments showed that blob detectors and spectra descriptors are the most suitable description technique to represent logos. They validated the performance of their approach on both standard and private databases, including the Tobaco800 set, and achieved significantly better performance than prior methods for logo spotting. More recent works, such as the one presented in [20], also show very promising results using a Siamese neural network. Logo detection, which is the extraction of logotypes inside a document with respect to letters and other graphical elements like signatures, has not received much attention from the research community, as far as we know. Ref. [21] used feature vectors with statistics about the connected components in the logo to construct a rule-based system that performed the classification. The main problem of this approach is deriving the rules to be applied. Ref. [22] is based on the segmentation and calculation of the spatial density of foreground pixels in document images. Ref. [23] presented a method for logo detection exploiting contour-based features using a learning-based method to score the strings of contours and used some rules to localize and correct the logo. The works of [24; 25] present a simple, dynamic approach to logo detection and recognition based on the boundary extension of feature rectangles in document images with a framework that reorganizes the structure of the logo-detection and -recognition procedures and integrates them. We consider this technique very promising for its simplicity and the good results it yields, but it still has problems when noisy or low-quality logos are used. Because of this deficiency, we focus the research by providing a new strategy to cope with these problems, which is structured into four steps, this being the main contribution of the paper. Authors such as [26] have shown good results in terms of detection recall and recognition accuracy for the Tobacco database. However, during training, they used a manually chosen subset of 100 images as representative of all logos and their locations, and therefore, it is not a fully automated system. In [27], logo detection was performed by preprocessing the input document, searching for connected components, and classifying these components into logo and non-logo candidates. The article [28] serves as a crucial benchmark in the field of logo detection using deep learning techniques. Through a comprehensive and systematic review, the authors comprehensively addressed the most relevant approaches, promising neural network architectures, and significant datasets in this domain. Moreover, they provided a critical analysis of current challenges and outlined future trends, positioning this work as a fundamental resource for researchers, academics, and professionals in the fields of computer vision and image processing.