# A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing
## 2 Related Work  
Automatic logo recognition has received special attention from previous research efforts, in particular as a special case of the more general problem of shape recognition. Logo detection represents a specialized branch within the broader scope of object detection. Its objective revolves around identifying instances of predefined logo classes in images or videos [1]. This section aims to delve into the current forefront of logo-detection methodologies. Prior to the advent of deep learning, early approaches to logo detection relied on hand-crafted visual features (e.g., SIFT and HOG) coupled with traditional classification models (e.g., SVM) [2; 3; 4; 5]. For instance, in 2013, Ref. [6] introduced a logo-matching system utilizing SIFT for logo detection and recognition. Their system not only facilitated the recognition and matching of multiple reference logos within an image, but also demonstrated efficacy in detecting similar logos. Nonetheless, these conventional methods exhibit certain limitations: (1) The region-selection search algorithm, predicated on sliding windows, lacks specificity, particularly with regard to high time complexity. (2) Hand-designed features exhibit limited robustness in accommodating variations in logo diversity. Shape descriptors can be classified as internal descriptors, if they code the boundary of a shape, or external descriptors, if they code the area enclosed by a boundary. A logotype is a rectangular region defined by a set of characteristics, which come from the pixel information and region shape, with values that differ from those of the text. These descriptors are used to discriminate logotypes from text. One of the first proponents of internal descriptors was [7], who used moments of area for shape description. Moments perform well for small and simple objects, but they tend to perform worse as objects become more complex or bigger. Regarding logotype recognition, some authors follow a statistical approach. For example, [PERSON]. [8] investigated logotype indexing and matching using invariant moments and the histogram of edge directions. [PERSON]. [9] proposed a logo-recognition method based on scale-invariant shape descriptors, considering logotypes as a distribution of points in 2D. A trademark-detection method based on a size function was presented by [10]. They tested the approach on a big set of trademark images with good results, but the authors pointed out that logos in noisy images are poorly detected. Our method tries to overcome that limitation. Other authors, like [11], follow a more structural approach where logotypes are first decomposed into sets of closed contours, each of which is coded according to chain-code information. Then, a string-matching algorithm is applied to compute similarities between the contours. [PERSON]. [12] used a hierarchical contour representation and string-matchingalgorithms to detect trademark pictograms. The downside of this method is that it is only appropriate when logotypes have the same hierarchical contour representation, which is rare. Neural networks were proposed in the context of logo recognition by authors like [13; 14]. The idea of this approach is to represent a logo using a structured representation called a contour tree. Contour trees associate a node with contour information extracted from logotype examples. Each node in the contour tree is labeled by a feature vector describing its curvature, perimeter, and other geometric properties. Then, a neural network is trained to learn from the examples represented by contour trees. The main advantage of this method is that it is very robust to changes in scale and imperfections. In our proposed method, we do not require a very high number of training examples, as a neural network does. Some authors, like [15], proposed a hybrid approach to logotype recognition using both global and local invariants. In their work, the use of global invariants serves to prune the logotype database initially, and then, the use of local invariants helps obtain a more precise match.