Cursive handwritten word recognition by integrating multiple classifiers

Maruyama, Kenichi; Kobayashi, M.; Yamada, Hitoshi; Nakano, Yasuaki

doi:10.1002/1520-684x(20001115)31:12<23::aid-scj3>3.0.co;2-7

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Vector Quantization1

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2016

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“…Codebook generated using k means clustering algorithm with 128 clusters, this number is the dominated in many researches [8,9].…”

Section: Vector Quantizationmentioning

confidence: 99%

Holistic Approach for Arabic Word Recognition

Wahbi¹,

Musa²

2016

IJCATR

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Abstract. Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large training dataset, researchers currently study Holistic Approach "recognition of a word without segmentation". This paper describes a method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also show that best number of state for each word need careful selection and considerations.

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“…Codebook generated using k means clustering algorithm with 128 clusters, this number is the dominated in many researches [8,9].…”

Section: Vector Quantizationmentioning

confidence: 99%