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Update text_detection_theory.ipynb
joaopabdala Apr 22, 2025
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Update text_detection_theory.ipynb
joaopabdala Apr 22, 2025
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4 changes: 2 additions & 2 deletions notebook_en/2.text_detection/text_detection_theory.ipynb
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"source": [
"## 1 Text Detection\n",
"\n",
"The task of text detection is to find out the position of text in an image or video. Different from the task of target detection, target detection must not only solve the positioning problem, but also solve the problem of target classification.\n",
"The task of text detection is to find out the position of text in an image or video. Different from the task of text detection, target detection must not only solve the positioning problem, but also solve the problem of target classification.\n",
"\n",
"The manifestation of text in images can be regarded as a kind of 'target', and general target detection methods are also suitable for text detection. From the perspective of the task itself:\n",
"\n",
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"\n",
"Although the segmentation method solves the problem of curved text detection, complex post-processing logic and prediction speed are also goals that need to be optimized.\n",
"\n",
"PAN [11] aims at the problem of slow text detection and prediction speed, and improves the performance of the algorithm from the aspects of network design and post-processing. First, PAN uses the lightweight ResNet18 as the Backbone, and also designs the lightweight feature enhancement module FPEM and feature fusion module FFM to enhance the features extracted by the Backbone. In terms of post-processing, a pixel clustering method is used to merge pixels whose distance from the kernel is less than the threshold d along the predicted text center (kernel). PAN guarantees high accuracy while having faster prediction speed.\n",
"PAN [11] aims at the problem of slow text detection and prediction speed, and improves the performance of the algorithm from the aspects of network design and post-processing. First, PAN uses the lightweight ResNet18 as the Backbone, and also designs the lightweight feature enhancement module FPEM and feature fusion module FFM to enhance the features extracted by the Backbone. In terms of post-processing, a pixel clustering method is used to merge pixels whose distance from the kernel is less than the threshold along the predicted text center (kernel). PAN guarantees high accuracy while having faster prediction speed.\n",
"\n",
"\n",
"<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/a76771f91db246ee8be062f96fa2a8abc7598dd87e6d4755b63fac71a4ebc170\"\n",
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