Symbol Level Groundtruthing Environment in OMR

A simple framework for evaluating OMR at the symbol level is presented. While a true evaluation of an OMR system requires a high-level analysis, the automation of which is a largely unsolved problem, many high-level errors are correlated to these more tractably-analyzed lowerlevel errors

Pilvipalvelupohjaisten tekstintunnistusjärjestelmien soveltuvuus tositteiden käsittelyyn

Tässä työssä tutkittiin, kuinka hyvin pilvipalvelupohjaiset tekstintunnistusjärjestelmät soveltuvat mobiililaitteilla kuvattujen tositteiden käsittelyyn. Työssä vertailtiin kahden eri palveluntarjoajan tekstintunnistusjärjestelmiä, joita olivat Google Cloud Vision ja Microsoft Azure Computer Vision. Vertailussa käytettävät tositteet valittiin eTasku Solutions Oy:n ylläpitämästä tositearkistosta. Soveltuvuusvertailun lisäksi työssä esiteltiin tekstintunnistusprosessin keskeisimmät vaiheet, joita ovat kuvien hankinta, esikäsittely, segmentointi, piirreirrotus, luokittelu sekä jälkiprosessointi. Lisäksi käytiin läpi tekstintunnistuksen historiaa ja esiteltiin merkittävimpiä tekstintunnistusjärjestelmiä eri vuosikymmeniltä. Soveltuvuusvertailun tuloksien perusteella selvisi, että mobiililaitteilla kuvattujen tositteiden käsittely pilvipalvelupohjaisilla tekstintunnistusjärjestelmillä on mahdollista. Erityisesti Google Cloud Vision -palvelun tulokset olivat lupaavia. Soveltuvuusvertailun avulla löydettiin myös muutamia tositteita, joiden tunnistamisessa palveluilla oli erityisesti ongelmia

Framework Of Page Segmentation For Mushaf Al-Quran Based On Multiphase Level Segmentation

This paper presents the framework of page segmentation for Mushaf Al-Quran based on Multiphase Level Segmentation (MLS).This study focuses to (a) extract multiform frame shape by using a novel technique Neighbouring Pixel Behaviors (NPB) and (b) segment text line by using a novel technique which is Hybrid Projection Based Neighbouring Properties (HPBNP).Since Mushaf Al-Quran pages are decorated with a different type of pattern and design of a decorative frame.Thus,the decoration frame must be properly to extract out from a page of Mushaf Al-Quran first before properly get only the text of Mushaf Al-Quran regardless of its decoration heterogeneity.Therefore,NPB technique was proposed to remove multiform frame shape from the page of Mushaf Al-Quran.While the text of Mushaf Al-Quran has a several of diacritical marks,hence it will block the process of segmenting text line.Therefore,HPBNP technique was proposed for segment overlapping text line that interfered by diacritical marks or the stroke of the Arabic word. Experimental results of the proposed technique is shown in this paper

Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms

this paper, we use the following five-step methodology to quantitatively compare the performance of page segmentation algorithms: 1) First, we create mutually exclusive training and test data sets with groundtruth, 2) we then select a meaningful and computable performance metric, 3) an optimization procedure is then used to search automatically for the optimal parameter values of the segmentation algorithms on the training data set, 4) the segmentation algorithms are then evaluated on the test data set, and, finally, 5) a statistical and error analysis is performed to give the statistical significance of the experimental results. In particular, instead of the ad hoc and manual approach typically used in the literature for training algorithms, we pose the automatic training of algorithms as an optimization problem and use the Simplex algorithm to search for the optimal parameter value. A paired-model statistical analysis and an error analysis are then conducted to provide confidence intervals for the experimental results of the algorithms. This methodology is applied to the evaluation of five page segmentation algorithms of which, three are representative research algorithms and the other two are well-known commercial products, on 978 images from the University of Washington III data set. It is found that the performance indices (average textline accuracy) of the Voronoi, Docstrum, and Caere segmentation algorithms are not significantly different from each other, but they are significantly better than that of ScanSoft's segmentation algorithm, which, in turn, is significantly better than that of X-Y cu

Development, evaluation and optimization of image based methods for monitoring crystallization processes

Ph.DDOCTOR OF PHILOSOPH

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy