Deep Learning-Driven Approach for Handwritten Chinese Character Classification

• URL: http://arxiv.org/abs/2401.17098v2
• Date: Wed, 27 Mar 2024 00:46:26 GMT
• Title: Deep Learning-Driven Approach for Handwritten Chinese Character Classification
• Authors: Boris Kriuk, Fedor Kriuk,
• Abstract summary: Handwritten character recognition is a challenging problem for machine learning researchers. With numerous unique character classes present, some data, such as Logographic Scripts or Sino-Korean character sequences, bring new complications to the HCR problem. This paper proposes a highly scalable approach for detailed character image classification by introducing the model architecture, data preprocessing steps, and testing design instructions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Handwritten character recognition (HCR) is a challenging problem for machine learning researchers. Unlike printed text data, handwritten character datasets have more variation due to human-introduced bias. With numerous unique character classes present, some data, such as Logographic Scripts or Sino-Korean character sequences, bring new complications to the HCR problem. The classification task on such datasets requires the model to learn high-complexity details of the images that share similar features. With recent advances in computational resource availability and further computer vision theory development, some research teams have effectively addressed the arising challenges. Although known for achieving high accuracy while keeping the number of parameters small, many common approaches are still not generalizable and use dataset-specific solutions to achieve better results. Due to complex structure, existing methods frequently prevent the solutions from gaining popularity. This paper proposes a highly scalable approach for detailed character image classification by introducing the model architecture, data preprocessing steps, and testing design instructions. We also perform experiments to compare the performance of our method with that of existing ones to show the improvements achieved.

Related papers

• Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing [71.29488677105127]
  Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. We propose a contrastive learning-based STR framework by leveraging synthetic and real unlabeled data without any human cost. Our method achieves SOTA performance (94.7% and 70.9% average accuracy on common benchmarks and Union14M-Benchmark.
  arXiv  Detail & Related papers  (2024-11-23T15:24:47Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• Few-shot Image Classification based on Gradual Machine Learning [6.935034849731568]
  Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples. We propose a novel approach based on the non-i.i.d paradigm of gradual machine learning (GML) We show that the proposed approach can improve the SOTA performance by 1-5% in terms of accuracy.
  arXiv  Detail & Related papers  (2023-07-28T12:30:41Z)
• Efficient Deep Clustering of Human Activities and How to Improve Evaluation [53.08810276824894]
  We present a new deep clustering model for human activity re-cog-ni-tion (HAR) In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models.
  arXiv  Detail & Related papers  (2022-09-17T14:12:42Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Learning from Few Examples: A Summary of Approaches to Few-Shot Learning [3.6930948691311016]
  Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Deep learning solutions suffer from data hunger and extensively high computation time and resources. Few-shot learning that could drastically reduce the turnaround time of building machine learning applications emerges as a low-cost solution.
  arXiv  Detail & Related papers  (2022-03-07T23:15:21Z)
• Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
  Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages. We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
  arXiv  Detail & Related papers  (2021-11-04T04:39:02Z)
• Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work [9.44316959798363]
  This work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem. The current findings show that the dataset is unbalanced and which class examples should be added more.
  arXiv  Detail & Related papers  (2021-08-06T12:45:15Z)
• Tensor feature hallucination for few-shot learning [17.381648488344222]
  Few-shot classification addresses the challenge of classifying examples given limited supervision and limited data. Previous works on synthetic data generation for few-shot classification focus on exploiting complex models. We investigate how a simple and straightforward synthetic data generation method can be used effectively.
  arXiv  Detail & Related papers  (2021-06-09T18:25:08Z)
• When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? [53.523017945443115]
  We describe natural prediction problems in which every sufficiently accurate training algorithm must encode, in the prediction model, essentially all the information about a large subset of its training examples. Our results do not depend on the training algorithm or the class of models used for learning.
  arXiv  Detail & Related papers  (2020-12-11T15:25:14Z)
• Robust Document Representations using Latent Topics and Metadata [17.306088038339336]
  We propose a novel approach to fine-tuning a pre-trained neural language model for document classification problems. We generate document representations that capture both text and metadata artifacts in a task manner. Our solution also incorporates metadata explicitly rather than just augmenting them with text.
  arXiv  Detail & Related papers  (2020-10-23T21:52:38Z)

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.