Chinese Handwriting Recognition: An Algorithmic Perspective

Su, Tonghua

doi:10.1007/978-3-642-31812-2

Cited by 21 publications

(9 citation statements)

References 98 publications

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“…To demonstrate that the DCNN-based methods can achieve better recognition accuracy, we use the ICDAR 2013 competition dataset [34] to conduct the experiment. We not only compared the proposed three models with the traditional methods [2,51,52] but also compared them with other DCNN-based methods [34,[38][39][40]. e comparison results are listed in Table 1.…”

Section: Scientific Programmingmentioning

confidence: 99%

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

Gao

Jin

2018

Scientific Programming

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Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.

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Section: Scientific Programmingmentioning

confidence: 99%

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

Gao

Jin

2018

Scientific Programming

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“…There exist many research works towards handwritten word recognition in Roman [1,6,17], Japanese/Chinese [2,3] and Arabic scripts [5]. To overcome the drawbacks of recognition approaches, word spotting technique [11,14,23,25] is used for information retrieval purpose.…”

Section: Introductionmentioning

confidence: 99%

Cross-language framework for word recognition and spotting of Indic scripts

Bhunia

Roy

Mohta

et al. 2018

Pattern Recognition

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Handwritten word recognition and spotting of low-resource scripts are difficult as sufficient training data is not available and it is often expensive for collecting data of such scripts. This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available script (considered as source script) and testing is done on other scripts (considered as target script). Training with one source script and testing with another script to have a reasonable result is not easy in handwriting domain due to the complex nature of handwriting variability among scripts. Also it is difficult in mapping between source and target characters when they appear in cursive word images. The proposed Indic cross language framework exploits a large resource of dataset for training and uses it for recognizing and spotting text of other target scripts where sufficient amount of training data is not available. Since, Indic scripts are mostly written in 3 zones, namely, upper, middle and lower, we employ zone-wise character (or component) mapping for efficient learning purpose. The performance of our crosslanguage framework depends on the extent of similarity between the source and target scripts. Hence, we devise an entropy based script similarity score using source to target character mapping that will provide a feasibility of cross language transcription. We have tested our approach in three Indic scripts, namely, Bangla, Devanagari and Gurumukhi, and the corresponding results are reported.

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“…HMMs avoid the problem of pre-segmentation of words into characters so that the errors of pre-segmentation can be eliminated. Character alignment based techniques for HMM is also studied to reduce the error [3]. Note that such approach was not applied for offline word recognition of Indic scripts earlier.Though, this character based HMM models are popular in the literature of word recognition, the process may not be directly useful in Indic scripts, especially in Devanagari and Bangla.…”

Section: Introductionmentioning

confidence: 99%

“…The main hindrance behind the difficulties of making a handwritten recognition system is the huge variation in writing style and complex shapes of characters in words. There are many research works towards handwritten word recognition in Roman [1], Japanese/Chinese [2,3] and Arabic scripts [4]. Although many investigations have been made towards the recognition of isolated handwritten characters and digits of Indian scripts [5], only a few pieces of work [6,7] exist towards offline handwritten word recognition in Indian scripts.…”

Section: Introductionmentioning

confidence: 99%

HMM-based Indic handwritten word recognition using zone segmentation

Roy

Bhunia

Das

et al. 2016

Pattern Recognition

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This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMMbased sequence modeling has been used earlier in holistic way. This paper proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone where characters are mostly touching are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters. Next, the residue components, if any, in upper and lower zones in their respective boundary are combined to achieve the final word level recognition. Water reservoir feature has been integrated in this framework to improve the zone segmentation and character alignment defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features has been compared with other existing features and found robust in Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition.Cite this as 1 @article{roy2016hmm,

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Chinese Handwriting Recognition: An Algorithmic Perspective

Cited by 21 publications

References 98 publications

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

Cross-language framework for word recognition and spotting of Indic scripts

HMM-based Indic handwritten word recognition using zone segmentation

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