Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition

• URL: http://arxiv.org/abs/2005.13044v1
• Date: Tue, 26 May 2020 21:15:20 GMT
• Title: Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition
• Authors: Lei Kang, Pau Riba, Mar\c{c}al Rusi\~nol, Alicia Forn\'es, Mauricio Villegas
• Abstract summary: We introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded.
• Score: 4.301658883577544
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios.

Related papers

• Efficiently Leveraging Linguistic Priors for Scene Text Spotting [63.22351047545888]
  This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive scene text spotting and recognition models. We generate text distributions that align well with scene text datasets, removing the need for in-domain fine-tuning. Experimental results show that our method not only improves recognition accuracy but also enables more accurate localization of words.
  arXiv  Detail & Related papers  (2024-02-27T01:57:09Z)
• Pixel Sentence Representation Learning [67.4775296225521]
  In this work, we conceptualize the learning of sentence-level textual semantics as a visual representation learning process. We employ visually-grounded text perturbation methods like typos and word order shuffling, resonating with human cognitive patterns, and enabling perturbation to be perceived as continuous. Our approach is further bolstered by large-scale unsupervised topical alignment training and natural language inference supervision.
  arXiv  Detail & Related papers  (2024-02-13T02:46:45Z)
• Text as Image: Learning Transferable Adapter for Multi-Label Classification [13.11583340598517]
  We introduce an effective approach to employ large language models for multi-label instruction-following text generation. In this way, a fully automated pipeline for visual label recognition is developed without relying on any manual data.
  arXiv  Detail & Related papers  (2023-12-07T09:22:20Z)
• A Transformer-based Approach for Arabic Offline Handwritten Text Recognition [0.0]
  We introduce two alternative architectures for recognizing offline Arabic handwritten text. Our approach can model language dependencies and relies only on the attention mechanism, thereby making it more parallelizable and less complex. Our evaluation on the Arabic KHATT dataset demonstrates that our proposed method outperforms the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-07-27T17:51:52Z)
• CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition [16.987008461171065]
  We explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task.
  arXiv  Detail & Related papers  (2023-03-16T14:27:45Z)
• Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition [101.60244147302197]
  We introduce contrastive learning and masked image modeling to learn discrimination and generation of text images. Our method outperforms previous self-supervised text recognition methods by 10.2%-20.2% on irregular scene text recognition datasets. Our proposed text recognizer exceeds previous state-of-the-art text recognition methods by averagely 5.3% on 11 benchmarks, with similar model size.
  arXiv  Detail & Related papers  (2022-07-01T03:50:26Z)
• FILIP: Fine-grained Interactive Language-Image Pre-Training [106.19474076935363]
  Fine-grained Interactive Language-Image Pre-training achieves finer-level alignment through a cross-modal late interaction mechanism. We construct a new large-scale image-text pair dataset called FILIP300M for pre-training. Experiments show that FILIP achieves state-of-the-art performance on multiple downstream vision-language tasks.
  arXiv  Detail & Related papers  (2021-11-09T17:15:38Z)
• SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators [67.54204685189255]
  We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
  arXiv  Detail & Related papers  (2021-05-21T18:34:21Z)
• Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild [103.51604161298512]
  We propose an adversarial learning framework for the generation and recognition of multiple characters in an image. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.
  arXiv  Detail & Related papers  (2020-01-13T12:41:42Z)

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.