Hippocampus-heuristic Character Recognition Network for Zero-shot Learning

• URL: http://arxiv.org/abs/2104.02236v1
• Date: Tue, 6 Apr 2021 01:57:20 GMT
• Title: Hippocampus-heuristic Character Recognition Network for Zero-shot Learning
• Authors: Shaowei Wang, Guanjie Huang, Xiangyu Luo
• Abstract summary: This paper proposes a novel Hippocampus-heuristic Character Recognition Network (HCRN) HCRN can recognize unseen Chinese characters (namely zero-shot learning) only by training part of radicals. It can accurately predict about 16,330 unseen testing Chinese characters relied on only 500 trained Chinese characters.
• Score: 3.720802292070508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recognition of Chinese characters has always been a challenging task due to their huge variety and complex structures. The latest research proves that such an enormous character set can be decomposed into a collection of about 500 fundamental Chinese radicals, and based on which this problem can be solved effectively. While with the constant advent of novel Chinese characters, the number of basic radicals is also expanding. The current methods that entirely rely on existing radicals are not flexible for identifying these novel characters and fail to recognize these Chinese characters without learning all of their radicals in the training stage. To this end, this paper proposes a novel Hippocampus-heuristic Character Recognition Network (HCRN), which references the way of hippocampus thinking, and can recognize unseen Chinese characters (namely zero-shot learning) only by training part of radicals. More specifically, the network architecture of HCRN is a new pseudo-siamese network designed by us, which can learn features from pairs of input training character samples and use them to predict unseen Chinese characters. The experimental results show that HCRN is robust and effective. It can accurately predict about 16,330 unseen testing Chinese characters relied on only 500 trained Chinese characters. The recognition accuracy of HCRN outperforms the state-of-the-art Chinese radical recognition approach by 15% (from 85.1% to 99.9%) for recognizing unseen Chinese characters.

Related papers

• Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slip Scripts [65.10991154918737]
  This study focuses on the Chu bamboo slip (CBS) script used during the Spring and Autumn and Warring States period (771-256 BCE) in Ancient China. Our tokenizer first adopts character detection to locate character boundaries, and then conducts character recognition at both the character and sub-character levels. To support the academic community, we have also assembled the first large-scale dataset of CBSs with over 100K annotated character image scans.
  arXiv  Detail & Related papers  (2024-09-02T07:42:55Z)
• HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text Recognition [47.86479271322264]
  We propose HierCode, a novel and lightweight codebook that exploits the innate hierarchical nature of Chinese characters. HierCode employs a multi-hot encoding strategy, leveraging hierarchical binary tree encoding and prototype learning to create distinctive, informative representations for each character. This approach not only facilitates zero-shot recognition of OOV characters by utilizing shared radicals and structures but also excels in line-level recognition tasks by computing similarity with visual features.
  arXiv  Detail & Related papers  (2024-03-20T17:20:48Z)
• Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through Image-IDS Aligning [61.34060587461462]
  We propose a two-stage framework for Chinese Text Recognition (CTR) We pre-train a CLIP-like model through aligning printed character images and Ideographic Description Sequences (IDS) This pre-training stage simulates humans recognizing Chinese characters and obtains the canonical representation of each character. The learned representations are employed to supervise the CTR model, such that traditional single-character recognition can be improved to text-line recognition.
  arXiv  Detail & Related papers  (2023-09-03T05:33:16Z)
• Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training [50.100992353488174]
  We introduce CDBERT, a new learning paradigm that enhances the semantics understanding ability of the Chinese PLMs with dictionary knowledge and structure of Chinese characters. We name the two core modules of CDBERT as Shuowen and Jiezi, where Shuowen refers to the process of retrieving the most appropriate meaning from Chinese dictionaries. Our paradigm demonstrates consistent improvements on previous Chinese PLMs across all tasks.
  arXiv  Detail & Related papers  (2023-05-30T05:48:36Z)
• Chinese Character Recognition with Radical-Structured Stroke Trees [51.8541677234175]
  We represent each Chinese character as a stroke tree, which is organized according to its radical structures. We propose a two-stage decomposition framework, where a Feature-to-Radical Decoder perceives radical structures and radical regions. A Radical-to-Stroke Decoder further predicts the stroke sequences according to the features of radical regions.
  arXiv  Detail & Related papers  (2022-11-24T10:28:55Z)
• Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition [4.64065792373245]
  We develop a stroke-based autoencoder to model the sophisticated morphology of Chinese characters. Our SAE architecture outperforms other existing methods in zero-shot recognition.
  arXiv  Detail & Related papers  (2022-07-17T14:39:10Z)
• Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension [9.66226932673554]
  Native Chinese Reader is a new machine reading comprehension dataset with particularly long articles in both modern and classical Chinese. NCR is collected from the exam questions for the Chinese course in China's high schools, which are designed to evaluate the language proficiency of native Chinese youth.
  arXiv  Detail & Related papers  (2021-12-13T09:11:38Z)
• ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information [32.70080326854314]
  We propose ChineseBERT, which incorporates the glyph and pinyin information of Chinese characters into language model pretraining. The proposed ChineseBERT model yields significant performance boost over baseline models with fewer training steps.
  arXiv  Detail & Related papers  (2021-06-30T13:06:00Z)
• Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition [37.808021793372504]
  We propose a stroke-based method by decomposing each character into a sequence of strokes. We employ a matching-based strategy to transform the predicted stroke sequence to a specific character. The proposed method can be easily generalized to other languages whose characters can be decomposed into strokes.
  arXiv  Detail & Related papers  (2021-06-22T08:49:03Z)
• SHUOWEN-JIEZI: Linguistically Informed Tokenizers For Chinese Language Model Pretraining [48.880840711568425]
  We study the influences of three main factors on the Chinese tokenization for pretrained language models. We propose three kinds of tokenizers: SHUOWEN (meaning Talk Word), the pronunciation-based tokenizers; 2) JIEZI (meaning Solve Character), the glyph-based tokenizers. We find that SHUOWEN and JIEZI tokenizers can generally outperform conventional single-character tokenizers.
  arXiv  Detail & Related papers  (2021-06-01T11:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.