Résumé

<p style="margin-left:0px;">Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g., libraries, office communication, management of workflows, and electronic archiving. Such a digitalization step plays an important role in decreasing costs and increasing the efficiency of document management systems.</p><p style="margin-left:0px;">Document management systems require document imaging/scanning approaches to convert hard-copy documents/images into digital files. However, document management systems are complex systems consisting of database servers and any document analysis related processes. The term document management refers to the database-supported management of electronic documents. A basic application of document management in the narrower sense is the digital files, in which information from various sources is either extracted or fused and refers to multiple system categories and their interaction in the broader sense.</p><p style="margin-left:0px;">Furthermore, the added value of such systems arises when documents have to be retrieved and/or analysed after some time due to legal requirements, and such a retrieval/analysis can be avoided or be related to financial penalties that can be significant for the industry. Moreover, costs and efforts can be reduced by retrieving documents. Increasingly, document imaging systems are being used as the base for organizational programs. The completion of tasks, orders, etc., is thus supported in logical and temporal sequences as workflows.</p><p style="margin-left:0px;">Since the conversion is not merely an image, Optical Character Recognition (OCR) is consecutively involved in recognizing and extracting the information contained in the document images. The documents can then be indexed and the extracted information can be transferred to a document management system for further processing. However, the OCR system does not show promising performance whenever images might be curved, distorted (e.g., by noise, blur, low contrast, and shadow), skewed, or have insufficient resolution, resulting in the loss of valuable image assets for character identification. Particularly hard distortion conditions occur nowadays when document images are acquired by using smart phone cameras. This means that while the image is accessible, the document might, however, not always be clearly readable.</p><p style="margin-left:0px;">In the state-of-the-art, there are many approaches to overcome the challenges of digital imaging/scanning systems: for example, utilizing self-learning systems with similarity/embedding vectors, neural models, and deep learning. Furthermore, pattern recognition can be used in two ways: (a) to determine the location of a predefined pattern in a larger image area, e.g., in a pick-and-place application where a vision system finds the object or the bar code and transmits the position to a robot; and (b) to focus classification on the nature of the visible object at a given location, e.g., in the case of text recognition where the position of each character is known but where it is necessary to determine which letter or digit is present.</p><p style="margin-left:0px;">Generally, high quality captured document images are required due to a series of challenges related to the performance of the visual sensors and, for camera-based captures, difficult external environmental conditions encountered during the sensing (image capturing) process. Such document images are mostly hard to read, have low contrast, and are corrupted by various artifacts such as noise, blur, shadows, spot lights, etc., just to name a few. To ensure an acceptable quality of the final document-images that can be perfectly digitalized and involved in various high-level applications based on digital documents, the sensing process must be made much more robust than the raw capture result generated by a purely physical visual sensor. Thus, the physical sensors must be virtually augmented by a series of additional pre-processing and/or post-processing functional blocks, which mostly involve, amongst others, advanced machine learning techniques.</p><p style="margin-left:0px;">This book emerging from the Special Issue "Document-Image Related Visual Sensors and Machine Learning Techniques” can be viewed as a result of the crucial need for document management systems. Such technologies are being applied in various fields or different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies. The Special Issue includes nine papers submitted in response to the call for papers. The Special Issue includes impactful papers that present scientific concepts, frameworks, architectures and innovative ideas on sensing technologies and machine-learning techniques to overcome the challenges of document imaging/scanning, test detection, text recognition and documents clustering.</p>