Robustness Design of Industrial Strength Recognition Systems

Fujisawa, Hiromichi

doi:10.1007/978-1-84628-726-8_9

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…[15]. More specific to the final recognition step is the pre-processing of the actual handwriting, where preprocessing operations are applied that attempt to normalize the appearance of the writing with respect to baseline orientation (frequently also referred to as skew), slant angle, and size (cf., e.g., [11]).…”

Section: Related Workmentioning

confidence: 99%

Camera-Based Whiteboard Reading for Understanding Mind Maps

Vajda

Plötz

Fink

2015

Int. J. Patt. Recogn. Artif. Intell.

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Mind maps, i.e. the spatial organization of ideas and concepts around a central topic and the visualization of their relations, represent a very powerful and thus popular means to support creative thinking and problem solving processes. Typically created on traditional whiteboards, they represent an important technique for collaborative brainstorming sessions. We describe a camera-based system to analyze hand-drawn mind maps written on a whiteboard. The goal of the presented system is to produce digital representations of such mind maps, which would enable digital asset management, i.e. storage and retrieval of manually created documents. Our system is based on image acquisition by means of a camera, followed by the segmentation of the particular whiteboard image focusing on the extraction of written context, i.e. the ideas captured by the mind map. The spatial arrangement of these ideas is recovered using layout analysis based on unsupervised clustering, which results in graph representations of mind maps. Finally, handwriting recognition derives textual transcripts of the ideas captured by the mind map. We demonstrate the capabilities of our mind map reading system by means of an experimental evaluation, where we analyze images of mind maps that have been drawn on whiteboards, without any further constraints other than the underlying topic. In addition to the promising recognition results, we also discuss training strategies, which effectively allow for system bootstrapping using out-of-domain sample data. The latter is important when addressing creative thinking processes where domain-related training data are difficult to obtain as they focus on novelty by definition.

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Section: Related Workmentioning

confidence: 99%

Camera-Based Whiteboard Reading for Understanding Mind Maps

Vajda

Plötz

Fink

2015

Int. J. Patt. Recogn. Artif. Intell.

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“…Moreover, during operations, the corresponding operating points -which may be selected dynamically according to the risk associated with the input samples -can significantly reduce the error rates. This approach follows the multiple-hypothesis principle [7], which request the system to propagate several hypothesis throughout the recognition steps, generating a hierarchical tree of possible solutions.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Hypothesis Approach for Off-Line Signature Verification with HMMs

Batista

Granger

Sabourin

2009

2009 10th International Conference on Document Analysis and Recognition

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“…Therefore, almost all HWR systems apply preprocessing operations that attempt to normalize the appearance of the writing with respect to these three aspects (cf., e.g., [53]). Usually, in some phase of preprocessing, the word or text line images are binarized separating dark ink pixels from the document's background (cf., e.g., [113]).…”

Section: Preprocessingmentioning

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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Robustness Design of Industrial Strength Recognition Systems

Cited by 7 publications

References 19 publications

Camera-Based Whiteboard Reading for Understanding Mind Maps

Camera-Based Whiteboard Reading for Understanding Mind Maps

A Multi-Hypothesis Approach for Off-Line Signature Verification with HMMs

Markov models for offline handwriting recognition: a survey

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