Hidden loop recovery for handwriting recognition

Doermann, David; Intrator, Nathan; Rivin, E.; Steinherz, T.

doi:10.1109/iwfhr.2002.1030939

Cited by 10 publications

(14 citation statements)

References 2 publications

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“…= …. (7) 3) Complete Trajectory (CT) retrieval accuracy: One offline character image may have multiple junction points. It may happen that it traverses one junction point perfectly but fails for others.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

KumarBhunia

Bhowmick

Bhunia

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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In this paper, we introduce a novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition. Generally, online acquisition approach has more advantage than its offline counterpart as the online technique keeps track of the pen movement. Hence, pen tip trajectory retrieval from offline text can bridge the gap between online and offline methods. Our proposed framework employs sequence to sequence model which consists of an encoder-decoder LSTM module. The proposed encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation. The output of the encoder is fed to a decoder LSTM and we get the successive coordinate points from every time step of the decoder LSTM. Although the sequence to sequence model is a popular paradigm in various computer vision and language translation tasks, the main contribution of our work lies in designing an endto-end network for a decade old popular problem in document image analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit dataset are used for our experiments. Our proposed method has achieved superior performance compared to the other conventional approaches.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

KumarBhunia

Bhowmick

Bhunia

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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“…9c presents the distance functions between the left and right sides of an external contour fragment, Figs. 9a and 9b, respectively, around the joint local maximum (see [46] for elaborations).…”

Section: Internal Contour Recoverymentioning

confidence: 99%

Offline Loop Investigation for Handwriting Analysis

Steinherz

Doermann

Rivlin

et al. 2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Resolution of different types of loops in handwritten script presents a difficult task and is an important step in many classic word recognition systems, writer modeling, and signature verification. When processing a handwritten script, a great deal of ambiguity occurs when strokes overlap, merge, or intersect. This paper presents a novel loop modeling and contour-based handwriting analysis that improves loop investigation. We show excellent results on various loop resolution scenarios, including axial loop understanding and collapsed loop recovery. We demonstrate our approach for loop investigation on several realistic data sets of static binary images and compare with the ground truth of the genuine online signal.

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“…The method allows the extraction of handwritten strokes but not their sequence. It also identifies retracing of strokes and restores hidden loops [4] when presence of such loops is obvious. The strokes extracted are represented in vector form as cubic splines which allows to model pen tip trajectory with high precision and eases extraction of many structural features of handwriting.…”

Section: Introductionmentioning

confidence: 99%

Three-stage handwriting stroke extraction method with hidden loop recovery

Pervouchine

Leedham

Melikhov

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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A method for extraction of strokes from handwriting characters and graphemes is presented. The method allows the modelling of the original pen tip trajectory close to that perceived by humans, thus allowing its use in writer identification and verification tasks. The method is also capable of identifying retraced strokes and recovering hidden loops. Strokes are represented as cubic splines. The method extracts strokes in three stages: vectorisation, merging of skeletal branches and loop recovery, and final adjustment of near-junction and loop pieces. The evaluation of the method is performed by using its results for structural feature extraction and writer classification based on the features.

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Hidden loop recovery for handwriting recognition

Abstract: Abstract

Cited by 10 publications

References 2 publications

Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network

Offline Loop Investigation for Handwriting Analysis

Three-stage handwriting stroke extraction method with hidden loop recovery

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