Automated Text line Segmentation and Table detection for Pre-Printed Document Image Analysis Systems

Rani, N. Shobha; Pruthvi, T. R; Rao, Aishwarya Govinda; Bipin, Nair B.J

doi:10.1109/icspc51351.2021.9451779

Cited by 5 publications

(1 citation statement)

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“…At present, the academic research of Uyghur text detection algorithms is still in its infancy. As a front-end task of character recognition, scene text detection is of great significance in the fields of electronic ancient book protection [2,3], intelligent transportation [4,5], etc. Modern Uyghur adopts an improved Arabic alphabet, which contains 32 basic letters and is written from right to left.…”

Section: Introductionmentioning

confidence: 99%

Scene Uyghur Text Detection Based on Fine-Grained Feature Representation

Wang

Mamat

et al. 2022

Sensors

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Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar texture and text morphology in the complex background noise of natural scene images are prone to false recall and difficult to detect multi-scale texts, a multi-directional scene Uyghur text detection model based on fine-grained feature representation and spatial feature fusion is proposed, and feature extraction and feature fusion are improved to enhance the network’s ability to represent multi-scale features. In this method, the multiple groups of 3 × 3 convolutional feature groups that are connected like the hierarchical residual to build a residual network for feature extraction, which captures the feature details and increases the receptive field of the network to adapt to multi-scale text and long glued dimensional font detection and suppress false positives of text-like objects. Secondly, an adaptive multi-level feature map fusion strategy is adopted to overcome the inconsistency of information in multi-scale feature map fusion. The proposed model achieves 93.94% and 84.92% F-measure on the self-built Uyghur dataset and the ICDAR2015 dataset, respectively, which improves the accuracy of Uyghur text detection and suppresses false positives.

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