DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for HMER

• URL: http://arxiv.org/abs/2306.16482v1
• Date: Wed, 28 Jun 2023 18:12:23 GMT
• Title: DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for HMER
• Authors: Aniket Pal, Krishna Pratap Singh
• Abstract summary: It is difficult to accurately determine the length and complex spatial relationships among symbols in handwritten mathematical expressions. In this study, we present a novel encoder-decoder architecture (DenseBAM-GI) for HMER. The proposed model is an efficient and lightweight architecture with performance equivalent to state-of-the-art models in terms of Expression Recognition Rate (exprate)
• Score: 4.518012967046983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of recognising Handwritten Mathematical Expressions (HMER) is crucial in the fields of digital education and scholarly research. However, it is difficult to accurately determine the length and complex spatial relationships among symbols in handwritten mathematical expressions. In this study, we present a novel encoder-decoder architecture (DenseBAM-GI) for HMER, where the encoder has a Bottleneck Attention Module (BAM) to improve feature representation and the decoder has a Gated Input-GRU (GI-GRU) unit with an extra gate to make decoding long and complex expressions easier. The proposed model is an efficient and lightweight architecture with performance equivalent to state-of-the-art models in terms of Expression Recognition Rate (exprate). It also performs better in terms of top 1, 2, and 3 error accuracy across the CROHME 2014, 2016, and 2019 datasets. DenseBAM-GI achieves the best exprate among all models on the CROHME 2019 dataset. Importantly, these successes are accomplished with a drop in the complexity of the calculation and a reduction in the need for GPU memory.

Related papers

• NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition [80.22784377150465]
  Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding. This paper makes the first attempt to build a novel bottom-up Non-AutoRegressive Modeling approach for HMER, called NAMER. NAMER comprises a Visual Aware Tokenizer (VAT) and a Parallel Graph (PGD)
  arXiv  Detail & Related papers  (2024-07-16T04:52:39Z)
• ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition [9.389169879626428]
  This paper introduces a novel approach, Implicit Character-Aided Learning (ICAL), to mine the global expression information. By modeling and utilizing implicit character information, ICAL achieves a more accurate and context-aware interpretation of handwritten mathematical expressions.
  arXiv  Detail & Related papers  (2024-05-15T02:03:44Z)
• Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition [51.66383337087724]
  The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR. Recent studies have demonstrated that incorporating bidirectional context information significantly improves the performance of HMER models. We propose the Mirror-Flipped Symbol Layout Tree (MF-SLT) and Bidirectional Asynchronous Training (BAT) structure.
  arXiv  Detail & Related papers  (2023-12-31T09:24:21Z)
• Low-Resolution Self-Attention for Semantic Segmentation [96.81482872022237]
  We introduce the Low-Resolution Self-Attention (LRSA) mechanism to capture global context at a significantly reduced computational cost. Our approach involves computing self-attention in a fixed low-resolution space regardless of the input image's resolution. We demonstrate the effectiveness of our LRSA approach by building the LRFormer, a vision transformer with an encoder-decoder structure.
  arXiv  Detail & Related papers  (2023-10-08T06:10:09Z)
• When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition [57.51793420986745]
  We propose an unconventional network for handwritten mathematical expression recognition (HMER) named Counting-Aware Network (CAN) We design a weakly-supervised counting module that can predict the number of each symbol class without the symbol-level position annotations. Experiments on the benchmark datasets for HMER validate that both joint optimization and counting results are beneficial for correcting the prediction errors of encoder-decoder models.
  arXiv  Detail & Related papers  (2022-07-23T08:39:32Z)
• MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs [55.66953093401889]
  Masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mask a large proportion of edges and try to reconstruct these missing edges during training.
  arXiv  Detail & Related papers  (2022-01-07T16:48:07Z)
• Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer [2.952085248753861]
  A transformer-decoder decoder is employed to replace RNN-based ones. Experiments demonstrate that our model improves the ExpRate of current state-of-the-art methods on CROHME 2014 by 2.23%.
  arXiv  Detail & Related papers  (2021-05-06T03:11:54Z)
• Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation [56.73834525802723]
  Lightweight Dynamic Graph Convolutional Networks (LDGCNs) are proposed. LDGCNs capture richer non-local interactions by synthesizing higher order information from the input graphs. We develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity.
  arXiv  Detail & Related papers  (2020-10-09T06:03:46Z)
• Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention [35.82648516972362]
  Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition. The performance of HMER suffers from the two-dimensional structure of mathematical expressions (MEs) We propose a high-performance HMER model with scale augmentation and drop attention.
  arXiv  Detail & Related papers  (2020-07-20T13:35:09Z)

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.