Text-Pass Filter: An Efficient Scene Text Detector

• URL: http://arxiv.org/abs/2601.18098v1
• Date: Mon, 26 Jan 2026 03:21:11 GMT
• Title: Text-Pass Filter: An Efficient Scene Text Detector
• Authors: Chuang Yang, Haozhao Ma, Xu Han, Yuan Yuan, Qi Wang,
• Abstract summary: We design Text-Pass Filter (TPF) for arbitrary-shaped text detection.<n>It segments the whole text directly, which avoids the intrinsic limitations.<n>TPF can separate adhesive texts naturally without complex decoding or post-processing processes.
• Score: 13.518443145609204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To pursue an efficient text assembling process, existing methods detect texts via the shrink-mask expansion strategy. However, the shrinking operation loses the visual features of text margins and confuses the foreground and background difference, which brings intrinsic limitations to recognize text features. We follow this issue and design Text-Pass Filter (TPF) for arbitrary-shaped text detection. It segments the whole text directly, which avoids the intrinsic limitations. It is noteworthy that different from previous whole text region-based methods, TPF can separate adhesive texts naturally without complex decoding or post-processing processes, which makes it possible for real-time text detection. Concretely, we find that the band-pass filter allows through components in a specified band of frequencies, called its passband but blocks components with frequencies above or below this band. It provides a natural idea for extracting whole texts separately. By simulating the band-pass filter, TPF constructs a unique feature-filter pair for each text. In the inference stage, every filter extracts the corresponding matched text by passing its pass-feature and blocking other features. Meanwhile, considering the large aspect ratio problem of ribbon-like texts makes it hard to recognize texts wholly, a Reinforcement Ensemble Unit (REU) is designed to enhance the feature consistency of the same text and to enlarge the filter's recognition field to help recognize whole texts. Furthermore, a Foreground Prior Unit (FPU) is introduced to encourage TPF to discriminate the difference between the foreground and background, which improves the feature-filter pair quality. Experiments demonstrate the effectiveness of REU and FPU while showing the TPF's superiority.

Related papers

• 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)
• 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)
• TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition [78.67283660198403]
  Text irregularities pose significant challenges to scene text recognizers. TPS++ is an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification. It consistently improves the recognition and achieves state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-05-09T10:16:43Z)
• 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)
• TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask [19.269070203448187]
  Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation. We propose a novel light-weight anchor-free text detection framework called TextDCT, which adopts the discrete cosine transform (DCT) to encode the text masks as compact vectors. TextDCT achieves F-measure of 85.1 at 17.2 frames per second (FPS) and F-measure of 84.9 at 15.1 FPS for CTW1500 and Total-Text datasets, respectively.
  arXiv  Detail & Related papers  (2022-06-27T15:42:25Z)
• Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection [47.820683360286786]
  We present a transformer-based architecture for scene text detection. We first select a few representative features at all scales that are highly relevant to foreground text. As each feature group corresponds to a text instance, its bounding box can be easily obtained without any post-processing operation.
  arXiv  Detail & Related papers  (2022-03-29T04:02:31Z)
• 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.