STEP -- Towards Structured Scene-Text Spotting

• URL: http://arxiv.org/abs/2309.02356v2
• Date: Mon, 11 Dec 2023 09:21:00 GMT
• Title: STEP -- Towards Structured Scene-Text Spotting
• Authors: Sergi Garcia-Bordils, Dimosthenis Karatzas, Mar\c{c}al Rusi\~nol
• Abstract summary: We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. We propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios.
• Score: 9.339184886724812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios.

Related papers

• Towards Unified Multi-granularity Text Detection with Interactive Attention [56.79437272168507]
  "Detect Any Text" is an advanced paradigm that unifies scene text detection, layout analysis, and document page detection into a cohesive, end-to-end model. A pivotal innovation in DAT is the across-granularity interactive attention module, which significantly enhances the representation learning of text instances. Tests demonstrate that DAT achieves state-of-the-art performances across a variety of text-related benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T07:25:23Z)
• Text Grouping Adapter: Adapting Pre-trained Text Detector for Layout Analysis [52.34110239735265]
  We present Text Grouping Adapter (TGA), a module that can enable the utilization of various pre-trained text detectors to learn layout analysis. Our comprehensive experiments demonstrate that, even with frozen pre-trained models, incorporating our TGA into various pre-trained text detectors and text spotters can achieve superior layout analysis performance.
  arXiv  Detail & Related papers  (2024-05-13T05:48:35Z)
• TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model [17.77384627944455]
  Existing scene text spotters are designed to locate and transcribe texts from images. Our proposed scene text spotter leverages advanced PLMs to enhance performance without fine-grained detection. Benefiting from the comprehensive language knowledge gained during the pre-training phase, the PLM-based recognition module effectively handles complex scenarios.
  arXiv  Detail & Related papers  (2024-03-15T06:38:25Z)
• ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting [8.397246652127793]
  We propose a new pre-training method called OCR-Text Destylization Modeling (ODM) ODM transfers diverse styles of text found in images to a uniform style based on the text prompt. Our method significantly improves performance and outperforms current pre-training methods in scene text detection and spotting tasks.
  arXiv  Detail & Related papers  (2024-03-01T06:13:53Z)
• 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)
• Contextual Text Block Detection towards Scene Text Understanding [85.40898487745272]
  This paper presents contextual text detection, a new setup that detects contextual text blocks (CTBs) for better understanding of texts in scenes. We formulate the new setup by a dual detection task which first detects integral text units and then groups them into a CTB. To this end, we design a novel scene text clustering technique that treats integral text units as tokens and groups them (belonging to the same CTB) into an ordered token sequence.
  arXiv  Detail & Related papers  (2022-07-26T14:59:25Z)
• Text Detection & Recognition in the Wild for Robot Localization [1.52292571922932]
  We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions.
  arXiv  Detail & Related papers  (2022-05-17T18:16:34Z)
• Towards End-to-End Unified Scene Text Detection and Layout Analysis [60.68100769639923]
  We introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way.
  arXiv  Detail & Related papers  (2022-03-28T23:35:45Z)
• TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text [23.04601165885908]
  We propose TextOCR, an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images. We show that current state-of-the-art text-recognition (OCR) models fail to perform well on TextOCR. We use a TextOCR trained OCR model to create PixelM4C model which can do scene text based reasoning on an image in an end-to-end fashion.
  arXiv  Detail & Related papers  (2021-05-12T07:50:42Z)
• 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.