Text-based Image Segmentation Methodology

AbstractIn computer vision, segmentation is the process of partitioning a digital image into multiple segments (sets of pixels). Image segmentation is thus inevitable. Segmentation used for text-based images aim in retrieval of specific information from the entire image. This information can be a line or a word or even a character. This paper proposes various methodologies to segment a text based image at various levels of segmentation. This material serves as a guide and update for readers working on the text based segmentation area of Computer Vision. First, the need for segmentation is justified in the context of text based information retrieval. Then, the various factors affecting the segmentation process are discussed. Followed by the levels of text segmentation are explored. Finally, the available techniques with their superiorities and weaknesses are reviewed, along with directions for quick referral are suggested. Special attention is given to the handwriting recognition since this area requires more advanced techniques for efficient information extraction and to reach the ultimate goal of machine simulation of human reading

A global method for music symbol recognition in typeset music sheets

International audienceThis paper presents an optical music recognition (OMR) system that can automatically recognize the main musical symbols of a scanned paper-based music score. Two major stages are distinguished: the first one, using low-level pre-processing, detects the isolated objects and outputs some hypotheses about them; the second one has to take the final correct decision, through high-level processing including contextual information and music writing rules. This article exposes both stages of the method: after explaining in detail the first one, the symbol analysis process, it shows through first experiments that its outputs can efficiently be used as inputs for a high-level decision process

Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords

Optical Music Recognition

Diplomová práce specifikuje digitální metody optického rozpoznávání notového záznamu s podrobnou analýzou metod založených na odstranění notových linek a vytvoření testovacího programu, který automaticky převede obrázky zapsané v notovém zápisu na digitální formát. Tato práce shrnuje poznatky jak z rešeršní, tak z praktické části. V rešeršní části jsou popsány stěžejní kapitoly jako architektura OMR zahrnující processing, klasifikace symbolů, postprocessing a další. Praktická část diplomové práce prezentuje výsledky vývoje a testování navržené aplikace.The diploma thesis specifies digital methods of optical recognition of a notation, by detailed analysis of methods based on removal of notation lines and creation of a test program which automatically converts the images written in the notation into digital format. This work summarizes the knowledge from the research and practical part. In the research section, key chapters are described as OMR architecture, including processing, symbol classification, postprocessing, and more. The practical part of the thesis presents the results of the development and testing of the proposed application.