EMANet: An Ancient Text Detection Method Based on Enhanced-EfficientNet and Multidimensional Scale Fusion
Peng Wang, Yongsheng Hu, Shuyun Peng, Luoyu Zhou
Abstract
Digitization through the Internet of Things (IoT) is a key measure for preserving ancient books. As a crucial aspect of digitization, text detection plays an essential role in the preservation and dissemination of ancient culture. Nevertheless, common detectors for ancient books struggle to meet the dual demands of speed and accuracy required by the IoT. Moreover, the robustness of these text detectors to capture complex features still needs to be strengthened. To tackle these issues, we introduce a new detector called EMANet. First, we substituted the feature extraction module in the core of EfficientNet-B3 with an improved Feature extraction (MBConv++) module to better capture the dependencies between channels. By incorporating this module, the network can concentrate on crucial features within the entire data sets. This significantly contributes to fulfilling the requirements for both speed and accuracy in ancient text detection. Additionally, we devised a multidimensional scale fusion (MDSF) module, effectively bolstering the scale robustness of the network. Finally, we construct a mobile ancient text digitization app suitable for the IoT. The proposed EMANet achieves an f-measure of 94.9% on shoot handwritten ancient book data set data sets, demonstrating its effectiveness. Simultaneously, we evaluate the generalization capability of EMANet using the public MTHv2 data sets. Results reveal that EMANet outperforms the majority of existing text detectors. Furthermore, our model demonstrates exceptional computational speed and precision in detection when deployed within the IoT framework, offering significant contributions to the holistic digitization of ancient texts.