Writer recognition of Arabic text by generative local features

Woodard, Jeffrey P.; Lancaster, M.; Kundu, A.; Ruiz, Dan; Ryan, John P.

doi:10.1109/btas.2010.5634495

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Figure 2). Woodward et al [35] first proposed to use an occurrence histogram of vector-quantized IP descriptors for writer identification, relying on probabilistic latent semantic analysis (pLSA) to identify the writer.…”

Section: Related Workmentioning

confidence: 99%

Simple and Fast Geometrical Descriptors for Writer Identification

Garz¹,

Würsch²,

Fischer³

et al. 2016

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Recent advances in writer identification push the limits by using increasingly complex methods relying on sophisticated preprocessing, or the combination of already complex descriptors. In this paper, we pursue a simpler and faster approach to writer identification, introducing novel descriptors computed from the geometrical arrangement of interest points at different scales. They capture orientation distributions and geometrical relationships of script parts such as strokes, junctions, endings, and loops. Thus, we avoid a fixed set of character appearances as in standard codebook-based methods. The proposed descriptors significantly cut down processing time compared to existing methods, are simple and efficient, and can be applied out-of-the-box to an unseen dataset. Evaluations on widely-used datasets show their potential when applied by themselves, and in combination with other descriptors. Limitations of our method relate to the amount of data needed to obtain reliable models.

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Section: Related Workmentioning

confidence: 99%

Simple and Fast Geometrical Descriptors for Writer Identification

Garz¹,

Würsch²,

Fischer³

et al. 2016

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“…al. 2011) and writer identification (Woodard, et al, 2010). While all these applications are not relevant to our application, we review the last paper due to its similarity to Rothacker's work.…”

Section: Implicit Segmentationmentioning

confidence: 99%

“…However, we followed some steps to choose other parameters as well. We explain these steps in details due to their importance to optimizing any statistical model (Woodard et al, 2010).…”

Section: A Statistical Viewmentioning

confidence: 99%

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Recognition of Arabic handwritten words

Alhmouz¹

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RECOGNITION OF ARABIC HANDWRITTEN WORDS by Omar Alhmouz Recognizing Arabic handwritten words is a difficult problem due to the deformations of different writing styles. Moreover, the cursive nature of the Arabic writing makes correct segmentation of characters an almost impossible task. While there are many sub systems in an Arabic words recognition system, in this work we develop a sub system to recognize Part of Arabic Words (PAW). We try to solve this problem using three different approaches, implicit segmentation and two variants of holistic approach. While Rothacker found similar conclusions while this work is being prepared, we report the difficulty in locating characters in PAW using Scale Invariant Feature Transforms under the first approach. In the second and third approaches, we use holistic approach to recognize PAW using Support Vector Machine (SVM) and Active Shape Models (ASM). While there are few works that use SVM to recognize PAW, they use a small dataset; we use a large dataset and a different set of features. We also explain the errors SVM and ASM make and propose some remedies to these errors as future work.

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