Continuous learning systems: postal address readers with built-in learning capability

Miletzki, Udo; Bayer, Tilman; Schäfer, Hermann

doi:10.1109/icdar.1999.791791

Cited by 8 publications

(5 citation statements)

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“…In some previous methods, the lexicon entries take a part in the recognition process, but the role is typically limited to filtering eligible prototypes for the targeted lexicon entry. For online adaptation of a reading system, a continuous learning system is presented in [11], which adapts to slow changes of writing habits and specific input specialties. The key idea is the tuning of system resources for changes in the global input stream.…”

Section: Introductionmentioning

confidence: 99%

An adaptive approach to offline handwritten word recognition

Park

2002

IEEE Trans. Pattern Anal. Machine Intell.

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AbstractÐAn adaptive handwritten word recognition method is presented. The key ideas of adaptation are 1) to actively and successively select a subset of features for each word image which provides the minimum required classification accuracy to get a valid answer and 2) to derive a consistent decision metric which works in a multiresolution feature space and considers the interrelationships of a lexicon at the same time. A recursive architecture based on interaction between flexible character classification and deductive decision making is developed. The recognition process starts from the initial coarse level using a minimum number of features, then increases the discrimination power by adding other features adaptively and recursively until the result is accepted by the decision maker. For the computational aspect of a feasible solution, a unified decision metric, recognition confidence, is derived from two measurements: pattern confidence, evaluation of absolute confidence using shape features, and lexical confidence, evaluation of the relative string dissimilarity in the lexicon. Practical implementation and experimental results in reading the handwritten words of the address components of US mail pieces are provided. Up to a 4 percent improvement in recognition performance is achieved compared to a nonadaptive method. The experimental result shows that the proposed method has advantages in producing valid answers using the same number of features as conventional methods.Index TermsÐPattern recognition, handwritten word recognition, adaptive word recognition.

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Section: Introductionmentioning

confidence: 99%

An adaptive approach to offline handwritten word recognition

Park

2002

IEEE Trans. Pattern Anal. Machine Intell.

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“…Automation of bank check processing is an important and promising application of document recognition techniques [2,3,[12][13][14]22]. It uses both recent theoretical achievements of pattern recognition and document analysis [5,6,10,15,16,24,28,30], and practical approaches developed in adjacent application areas, such as postal automation or form recognition [3,4,8,25,29]. A comprehensive overview of the stateof-the-art in the field of check recognition can be found in [2].…”

Section: Introductionmentioning

confidence: 99%

Industrial bank check processing: the A2iA CheckReaderTM

Gorski¹,

Anisimov²,

Augustin³

et al. 2001

IJDAR

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This paper presents the current state of the A2iA CheckReader TM -a commercial bank check recognition system. The system is designed to process the flow of payment documents associated with the check clearing process: checks themselves, deposit slips, money orders, cash tickets, etc. It processes document images and recognizes document amounts whatever their style and type -cursive, hand-or machine printed -expressed as numerals or as phrases. The system is adapted to read payment documents issued in different English-or Frenchspeaking countries. It is currently in use at more than 100 large sites in five countries and processes daily over 10 million documents. The average read rate at the document level varies from 65 to 85% with a misread rate corresponding to that of a human operator (1%).

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“…In recent years, tremendous efforts have been directed toward this issue (cf., e.g., [17,68,97,114]). It has resulted in powerful recognition systems, which are successfully applied by major postal service companies (cf., e.g., [54,90,103]). …”

Section: Applicationsmentioning

confidence: 99%

Markov models for offline handwriting recognition: a survey

Plötz

Fink

2009

IJDAR

191

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Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-modelbased offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

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Continuous learning systems: postal address readers with built-in learning capability

Cited by 8 publications

References 1 publication

An adaptive approach to offline handwritten word recognition

An adaptive approach to offline handwritten word recognition

Industrial bank check processing: the A2iA CheckReaderTM

Markov models for offline handwriting recognition: a survey

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