Increasing robustness of handwriting recognition using character N-Gram decoding on large lexica

Offline handwriting recognition systems often include a decoding step, that is retrieving the most likely character sequence from the underlying machine learning algorithm. Decoding is sensitive to ranges of weakly predicted characters, caused e.g. by obstructions in the scanned document. We present a new algorithm for robust decoding of handwriting recognizer outputs using character n-grams. Multidimensional hierarchical subsampling artificial neural networks with Long-Short-Term-Memory cells have been successfully applied to offline handwriting recognition. Output activations from such networks, trained with Connectionist Temporal Classification, can be decoded with several different algorithms in order to retrieve the most likely literal string that it represents. We present a new algorithm for decoding the network output while restricting the possible strings to a large lexicon. The index used for this work is an n-gram index with tri-grams used for experimental comparisons. N-grams are extracted from the network output using a backtracking algorithm and each n-gram assigned a mean probability. The decoding result is obtained by intersecting the n-gram hit lists while calculating the total probability for each matched lexicon entry. We conclude with an experimental comparison of different decoding algorithms on a large lexicon

Automatische Modellierung gebundener Handschrift in einem HMM-basierten Erkennungssystem

Zur Erkennung von Handschrift benötigt man ein gutes Modell. In gängigen Systemen zur automatischen Schrifterkennung wird dieses fest vorgegeben, indem grundlegendes Wissen über die Struktur der Schrift (Anzahl relevanter Schreibvarianten, ihre Größe und Komplexität) in die Struktur von Buchstabenmodellen einfließt. In deren Parametern wird das Aussehen der Zeichen dann repräsentiert und trainiert. Die vorliegende Arbeit beschäftigt sich hingegen mit der automatischen Bestimmung des richtigen Modells selbst. Grundlage ist ein Erkennungssystem für gebundene Handschrift, das Buchstaben durch Hidden-Markov-Modelle (HMMs) repräsentiert. Die Struktur des Schriftmodells ist in der Topologie der Buchstaben-HMMs angelegt. Diese gilt es zu optimieren. Dabei stellt sich die Frage nach einem Optimierungskriterium als Kompromiss zwischen Komplexität und Einfachheit. Es wird ein Ansatz nach Bayes verwendet, aus dem anhand von Näherungen verschiedene Modellwahlkriterien hergeleitet werden. Solche Kriterien werden in Zusammenhang mit HMMs üblicherweise nur auf einfache Modelle angewendet; hier werden sie an das komplexe System der Buchstaben-HMMs angepasst. Ihre Relevanz wird anhand der Erkennungsleistung untersucht und bestätigt. Neben der globalen Bewertung des vollständigen Schriftmodells werden weitere Maße für die lokale Bewertung und den Vergleich einzelner Buchstaben-HMMs vorgestellt. Für das HMM-Schriftmodell werden spezielle Verfahren zur Strukturoptimierung entwickelt. Einzelne Aspekte der Modellierung (Buchstabenbreite und -anzahl) werden gezielt und voneinander unabhängig optimiert. Beide Verfahren arbeiten iterativ und lassen sich durch sequentielle Ausführung zu einem Gesamtsystem kombinieren. Das System ist geeignet, automatisch Modelle zu finden, die plausibel sind und gute Erkennungsleistung zeigen. Gängige Schreibweisen werden identifiziert und im Durchschnitt steigt die Erkennungsrate um 8,1 Prozent. Das System wird in der Postautomatisierung erfolgreich eingesetzt

Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method

Recent years have seen the proposal of several different gradient-based optimization methods for training artificial neural networks. Traditional methods include steepest descent with momentum, newer methods are based on per-parameter learning rates and some approximate Newton-step updates. This work contains the result of several experiments comparing different optimization methods. The experiments were targeted at offline handwriting recognition using hierarchical subsampling networks with recurrent LSTM layers. We present an overview of the used optimization methods, the results that were achieved and a discussion of why the methods lead to different results

Dissecting multi-line handwriting for multi-dimensional connectionist classification

Multi-Dimensional Connectionist Classification is amethod for weakly supervised training of Deep Neural Networksfor segmentation-free multi-line offline handwriting recognition.MDCC applies Conditional Random Fields as an alignmentfunction for this task. We discuss the structure and patterns ofhandwritten text that can be used for building a CRF. Since CRFsare cyclic graphical models, we have to resort to approximateinference when calculating the alignment of multi-line text duringtraining, here in the form of Loopy Belief Propagation. This workconcludes with experimental results for transcribing small multi-line samples from the IAM Offline Handwriting DB which showthat MDCC is a competitive methodology

Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms

In document analysis the availability of ground truth data plays a crucial role for the success of a project. This is even more true at the rise of new deep learning methods which heavily rely on the availability of training data. But even for traditional, hand crafted algorithms that are not trained on data, reliable test data is important for the improvement and evaluation of the methods. Because ground truth acquisition is expensive and time consuming, semi-automatic methods are introduced which make use of suggestions coming from document analysis systems. The interaction between the human operator and the automatic analysis algorithms is the key to speed up the process while improving the quality of the data. The final confirmation of data may always be done by the human operator. This paper demonstrates a use case for acquisition of truth data in a mail processing system. It shows why a new, extended view on truth data is necessary in development and engineering of such systems. An overview over the tool and the data handling is given, the advantages in the workflow are shown, and consequences for the construction of analysis algorithms are discussed. It can be shown that the interplay between suggest algorithms and human operator leads to very fast truth data capturing. The surprising finding is the fact that if multiple suggest algorithms circularly depend on data, they are especially effective in terms of speed and accuracy

Performance of the ALICE Electromagnetic Calorimeter

International audienceThe performance of the electromagnetic calorimeter of theALICE experiment during operation in 2010–2018 at the Large HadronCollider is presented. After a short introduction into the design,readout, and trigger capabilities of the detector, the proceduresfor data taking, reconstruction, and validation are explained. Themethods used for the calibration and various derived corrections arepresented in detail. Subsequently, the capabilities of thecalorimeter to reconstruct and measure photons, light mesons,electrons and jets are discussed. The performance of thecalorimeter is illustrated mainly with data obtained with test beamsat the Proton Synchrotron and Super Proton Synchrotron or inproton-proton collisions at √s = 13 TeV, and compared tosimulations