Conjoined location and recognition of street names within a postal address delivery line

Yacoubi, Mounîm El; Bertille, J.-M.; Gilloux, M.

doi:10.1109/icdar.1995.602077

Cited by 16 publications

(5 citation statements)

References 5 publications

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“…An alternative approach is proposed in [17] [18], where a keyword spotting technique is used to detect the name of the street. The important advantage of such approach is that no segmentation of the line into words is required.…”

Section: Previous Workmentioning

confidence: 99%

Offline recognition of unconstrained handwritten texts using HMMs and statistical language models

Bunke

Bengio

Vinciarelli

2004

IEEE Trans. Pattern Anal. Machine Intell.

229

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This paper presents a system for the offline recognition of large vocabulary unconstrained handwritten texts. The only assumption made about the data is that it is written in English. This allows the application of Statistical Language Models in order to improve the performance of our system. Several experiments have been performed using both single and multiple writer data. Lexica of variable size (from 10,000 to 50,000 words) have been used. The use of language models is shown to improve the accuracy of the system (when the lexicon contains 50,000 words, error rate is reduced by ∼50% for single writer data and by ∼25% for multiple writer data). Our approach is described in detail and compared with other methods presented in the literature to deal with the same problem. An experimental setup to correctly deal with unconstrained text recognition is proposed.

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Section: Previous Workmentioning

confidence: 99%

Offline recognition of unconstrained handwritten texts using HMMs and statistical language models

Bunke

Bengio

Vinciarelli

2004

IEEE Trans. Pattern Anal. Machine Intell.

229

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“…An upgrade to the previous system is presented in El Yacoubi et al [86,87]. Each transition (besides a null one) turns into a double transition with a state between the two.…”

Section: Hmmsmentioning

confidence: 99%

Offline cursive script word recognition ? a survey

Steinherz

Rivlin

Intrator

1999

International Journal on Document Analysis and Recognition

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Abstract.We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to bootstrap a few candidates for a final evaluation phase.

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“…However, handwriting systems have been successfully applied to different tasks, such as bank cheque recognition 1 and postal address recognition. 2 Indeed bank cheques and addresses follow a strict format and have a very limited vocabulary: the prior knowledge on these task is extensively used to boost the recognition performances. However, the recognition of unconstrained handwriting that can occur in various documents, such as letters, books, notes, is a very complex task.…”

Section: Introductionmentioning

confidence: 99%

Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

et al. 2015

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The BLSTM-CTC is a novel recurrent neural network architecture that has outperformed previous state of the art algorithms in tasks such as speech recognition or handwriting recognition. It has the ability to process long term dependencies in temporal signals in order to label unsegmented data. This paper describes different ways of combining features using a BLSTM-CTC architecture. Not only do we explore the low level combination (feature space combination) but we also explore high level combination (decoding combination) and mid-level (internal system representation combination). The results are compared on the RIMES word database. Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

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Conjoined location and recognition of street names within a postal address delivery line

Cited by 16 publications

References 5 publications

Offline recognition of unconstrained handwritten texts using HMMs and statistical language models

Offline recognition of unconstrained handwritten texts using HMMs and statistical language models

Offline cursive script word recognition ? a survey

Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

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