Abstract
In fish farms a major issue is the net cage wear, resulting in fish escapes and negative impact of fish quality, due to holes and biofouling of the nets. To minimize fish losses, fisheries utilize divers to inspect net cages on a weekly basis. Aquaculture companies are looking for ways to maximize profit and reduce maintenance costs is one of them. Kefalonia Fisheries spend 250 thousand euros yearly on diver expenses for net cages maintenance. This work is about the design, fabrication, and control of an inexpensive autonomous underwater vehicle intended for inspection in net cages at Kefalonia Fisheries S.A. in Greece. Its main body is 3D-printed, and its eight-thruster configuration grants it six degrees of freedom. The main objective of the vehicle is to limit maintenance costs by increasing inspection frequency. The design, fabrication as well as the electronics and software architecture of the vehicle are presented. In addition, the forces affecting Kalypso, mobility realization, navigation, and modeling are quoted along with a flow simulation and the experimental results. The proposed design is adaptable and durable while remaining cost effective, and it can be used for both manual and automatic operations.

Abstract
Slanted text has been demonstrated to be a salient feature of handwriting. Its estimation is a necessary preprocessing task in many document image processing systems in order to improve the required training. This paper describes and evaluates a new technique for removing the slant from historical document pages that avoids the segmentation procedure into text lines and words. The proposed technique first relies on slant angle detection from an accurate selection of fragments. Then, a slant removal technique is applied. However, the presented slant removal technique may be combined with any other slant detection algorithm. Experimental results are provided for four document image databases: two historical document databases, the TrigraphSlant database (the only database dedicated to slant removal), and a printed database in order to check the precision of the proposed technique.

Abstract
A technique appropriate for extracting textual information from documents with complex layouts, such as newspapers and journals, is presented. It is a combination of a foreground analysis and a text localization method. The first one is used to segment the page in text and nontext blocks, whereas the second one is used to detect text that may be embedded inside images, charts, diagrams, tables, etc. Detailed experiments on two public databases showed that mixing layout analysis and text localization techniques can lead to improved page segmentation and text extraction results.

Abstract
In this paper, a hybrid technique for complex layout analysis is presented. Morphological operations are applied to both the foreground and the background, in order to connect neighboring regions and detect separator lines and columns respectively. Contour tracing is used for the extraction of shape and size information and classification of the connected components. Evaluated on the RDCL-2015 dataset, the method achieved state-of-art results in less than three seconds per page.

Abstract
The File Forgery Detection tasks is in its first edition, in 2019. This year, it is composed by three subtasks: a) Forged file discovery, b) Stego image discovery and c) Secret message discovery. The data set contained 6,400 images and pdf files, divided into 3 sets. There were 61 participants and the majority of them participated in all the subtasks. This highlights the major concern the scientific community shows for security issues and the importance of each subtask. Submissions varied from a) 8, b) 31 and c) 14 submissions for each subtask, respectively. Although the datasets were small, most of the participants used deep learning techniques, especially in subtasks 2 & 3. The results obtained in subtask 3 -which was the most difficult one- showed that there is room for improvement, as more advanced techniques are needed to achieve better results. Deep learning techniques adopted by many researchers is a preamble in that direction, and proved that they may provide a promising steganalysis tool to a digital forensics examiner.

Abstract
A system for extracting the textual information from document images with complex layouts is presented. It is based on both layout analysis and text localization techniques. Layout analysis is first applied to segment the page in text and non-text blocks and then text localization is used to detect text that may be embedded inside images, charts, diagrams, tables etc. Detailed experiments on scanned Arabic newspapers showed that combining layout analysis and text localization methods can lead to improved page segmentation and text extraction results.

Abstract
In order to convert a document image in its editable version, an OCR engine must identify and separate the nontext regions from text regions of a given document image. In the present work, a technique is developed to classify various text and non-text regions present in a document image. For that purpose, a modified version of Histogram of Oriented Gradient (HOG) is used as a feature descriptor. Multi-Layer Perceptron (MLP) is chosen from a pool of classifier by comparing the recognition accuracy of it with two other well-known classifiers viz., Random Forest (RF), Nave Bayes (NB). The designed technique is evaluated on a dataset, containing 862 images of manually extracted regions from two standard databases namely, RDCL2015 dataset and Media Team Document dataset. The proposed system has achieved 96.44% recognition accuracy and outperformed some of the state-of-the-art feature descriptors …