Retrieval-based language model adaptation for handwritten Chinese text recognition

Hu, Shuying; Wang, Qiufeng; Huang, Kaizhu; Wen, Min; Coenen, Frans

doi:10.1007/s10032-022-00419-2

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…In addition, they devised a contextual regularization method that incorporates integrated contextual information in the training of the FCN, thus significantly improving the recognition performance. The latest research is Hu et al [80], who proposed a new retrieval-based approach that dynamically retrieves relevant content of the recognized text from the Internet to train an adaptive language model (LM) that can be integrated into the whole recognition process. Their strategy goes through a two-pass recognition process.…”

Section: Segmentation-based Recognitionmentioning

confidence: 99%

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

et al. 2023

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Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.

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Section: Segmentation-based Recognitionmentioning

confidence: 99%

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

et al. 2023

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“…Document Layout Analysis (DLA) is a fundamental task in Document Understanding (DU) due to its extensive applications ranging from Key Information Extraction [34], Document Retrieval [21], Visual Question Answering [15], and so on. It helps to extract the semantic information from documents when parsing them into a structured machine-readable format.…”

Section: Introductionmentioning

confidence: 99%

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

Banerjee,

Biswas,

Lladós

et al. 2024

IJDAR

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Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach.

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An unsupervised automatic organization method for Professor Shirakawa’s hand-notated documents of oracle bone inscriptions

Yue,

Wang,

Ishibashi

et al. 2024

IJDAR

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As one of the most influential Chinese cultural researchers in the second half of the twentieth-century, Professor Shirakawa is active in the research field of ancient Chinese characters. He has left behind many valuable research documents, especially his hand-notated oracle bone inscriptions (OBIs) documents. OBIs are one of the world’s oldest characters and were used in the Shang Dynasty about 3600 years ago for divination and recording events. The organization of OBIs is not only helpful in better understanding Prof. Shirakawa’s research and further study of OBIs in general and their importance in ancient Chinese history. This paper proposes an unsupervised automatic organization method to organize Prof. Shirakawa’s OBIs and construct a handwritten OBIs data set for neural network learning. First, a suite of noise reduction is proposed to remove strangely shaped noise to reduce the data loss of OBIs. Secondly, a novel segmentation method based on the supervised classification of OBIs regions is proposed to reduce adverse effects between characters for more accurate OBIs segmentation. Thirdly, a unique unsupervised clustering method is proposed to classify the segmented characters. Finally, all the same characters in the hand-notated OBIs documents are organized together. The evaluation results show that noise reduction has been proposed to remove noises with an accuracy of 97.85%, which contains number information and closed-loop-like edges in the dataset. In addition, the accuracy of supervised classification of OBIs regions based on our model achieves 85.50%, which is higher than eight state-of-the-art deep learning models, and a particular preprocessing method we proposed improves the classification accuracy by nearly 11.50%. The accuracy of OBIs clustering based on supervised classification achieves 74.91%. These results demonstrate the effectiveness of our proposed unsupervised automatic organization of Prof. Shirakawa’s hand-notated OBIs documents. The code and datasets are available at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.

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Retrieval-based language model adaptation for handwritten Chinese text recognition

Cited by 6 publications

References 53 publications

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

An unsupervised automatic organization method for Professor Shirakawa’s hand-notated documents of oracle bone inscriptions

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