Japanese Cursive Character Recognition for Efficient Transcription

Ueki, Keisuke; Kojima, Tomoka

doi:10.5220/0008913204020406

Cited by 3 publications

(6 citation statements)

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“…The eight-point difference between the average classification accuracy and the overall accuracy shows how the more frequent classes are classified more accurately and bias the overall result. These results improve those previously presented in [28] using a Lenet network. Our subset of the Kuzushiji dataset is comparable to the one where [28] obtained 73.10% accuracy (considering classes with at least 20 examples but without capping the number of examples for larger classes).…”

Section: Kanji Classificationsupporting

confidence: 88%

“…These results improve those previously presented in [28] using a Lenet network. Our subset of the Kuzushiji dataset is comparable to the one where [28] obtained 73.10% accuracy (considering classes with at least 20 examples but without capping the number of examples for larger classes). Our results, however, reach the values of those reported in [28] by filtering "difficult to classify characters".…”

Section: Kanji Classificationsupporting

confidence: 88%

“…Our subset of the Kuzushiji dataset is comparable to the one where [28] obtained 73.10% accuracy (considering classes with at least 20 examples but without capping the number of examples for larger classes). Our results, however, reach the values of those reported in [28] by filtering "difficult to classify characters". The effects of re-balancing the dataset as well as the use of networks with a larger number of layers become apparent in the 81.72% average classification accuracy for all classes obtained by our densenet network.…”

Section: Kanji Classificationmentioning

confidence: 87%

“…This survey includes detailed analysis of the results of two recent character segmentation and recognition contests as well as a succinct overview of the database itself from the point off view of image analysis. Of most direct interest for our current goals is [28], where the classification of historical kanji is tackled. A LeNet [29] network was used to classify a subset of the Kuzushiji dataset that had been stripped of the classes with less than 20 examples and re-balanced using data augmentation.…”

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confidence: 99%

“…Although DL networks are clearly also capable of classifying the characters it contains, rebalancing the dataset in a way that fits the final application is mandatory. In [28], the authors define some "difficult to classify" characters and obtain a final classification accuracy over 90% for the remaining classes.…”

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confidence: 99%

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Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

et al. 2021

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“Wasan” is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the "ima" kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification.Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the "ima" kanji with 3% False positives.

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Section: Kanji Classificationsupporting

confidence: 88%

Section: Kanji Classificationsupporting

confidence: 88%

Section: Kanji Classificationmentioning

confidence: 87%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

et al. 2021

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Survey on Deep Learning-Based Kuzushiji Recognition

Ueki

Kojima

2021

Lecture Notes in Computer Science

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Survey on Deep Learning-based Kuzushiji Recognition

Ueki

Kojima

2020

Preprint

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Owing to the overwhelming accuracy of the deep learning method demonstrated at the 2012 image classification competition, deep learning has been successfully applied to a variety of other tasks. The high-precision detection and recognition of Kuzushiji, a Japanese cursive script used for transcribing historical documents, has been made possible through the use of deep learning. In recent years, competitions on Kuzushiji recognition have been held, and many researchers have proposed various recognition methods. This study examines recent research trends, current problems, and future prospects in Kuzushiji recognition using deep learning.

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Japanese Cursive Character Recognition for Efficient Transcription

Cited by 3 publications

References 0 publications

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

Survey on Deep Learning-Based Kuzushiji Recognition

Survey on Deep Learning-based Kuzushiji Recognition

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