Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer

• URL: http://arxiv.org/abs/2202.05508v2
• Date: Mon, 14 Feb 2022 05:55:25 GMT
• Title: Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer
• Authors: Yair Kittenplon, Inbal Lavi, Sharon Fogel, Yarin Bar, R. Manmatha, Pietro Perona
• Abstract summary: We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting. TTS is trained with both fully- and weakly-supervised settings. trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.
• Score: 21.479222207347238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.

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)
• SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting [126.01629300244001]
  We propose a new end-to-end scene text spotting framework termed SwinTextSpotter v2. We enhance the relationship between two tasks using novel Recognition Conversion and Recognition Alignment modules. SwinTextSpotter v2 achieved state-of-the-art performance on various multilingual (English, Chinese, and Vietnamese) benchmarks.
  arXiv  Detail & Related papers  (2024-01-15T12:33:00Z)
• ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer [88.61312640540902]
  We introduce Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter) Our model achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-20T03:22:23Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Decoupling Recognition from Detection: Single Shot Self-Reliant Scene Text Spotter [34.09162878714425]
  We propose the single shot Self-Reliant Scene Text Spotter (SRSTS) We conduct text detection and recognition in parallel and bridge them by the shared positive anchor point. Our method is able to recognize the text instances correctly even though the precise text boundaries are challenging to detect.
  arXiv  Detail & Related papers  (2022-07-15T01:59:14Z)
• SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition [73.61592015908353]
  We propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism. The design results in a concise framework that requires neither additional rectification module nor character-level annotation.
  arXiv  Detail & Related papers  (2022-03-19T01:14:42Z)
• Text Recognition -- Real World Data and Where to Find Them [36.10220484561196]
  We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. It produces nearly error-free, localised instances of scene text, which we treat as "pseudo ground truth" (PGT)
  arXiv  Detail & Related papers  (2020-07-06T22:23:27Z)
• Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting [49.768327669098674]
  We propose an end-to-end trainable text spotting approach named Text Perceptron. It first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies.
  arXiv  Detail & Related papers  (2020-02-17T08:07:19Z)

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.