Unstructured Data Parsing Method Based on Asymmetric Convolution and 3D Attention Residual Networks

Efficient parsing of manufacturing process data is a key enabler for the informatization of intelligent manufacturing systems. However, network isolation in aerospace-specific job shops makes large volumes of unstructured shop-floor data, such as handwritten production reports and equipment logs, inaccessible to existing information systems. To tackle this, we propose a comprehensive parsing framework that covers data acquisition, parsing, and structured output. For handwritten report parsing, we devise a collaborative pipeline comprising text detection via the Differentiable Binarization Network (DBNet); text recognition using an enhanced Convolutional Recurrent Neural Network (CRNN) that incorporates Asymmetric Convolution (AC) and a Simple Attention Module (SimAM)-based residual module (SimRes, short for SimAM ResNet), referred to as AC-SimRes-CRNN; and table structure extraction via TableMaster. The predicted table cell coordinates, detected text-region coordinates, and recognized text contents are subsequently aggregated to reconstruct complete tables, which are then exported as Excel files. Experiments on the CASIA-HWDB2x and IAM datasets show that AC-SimRes-CRNN achieves an Accurate Rate (AR) of 91.36% and a Correct Rate (CR) of 93.17% on Chinese handwritten text recognition and a Character Error Rate (CER) of 7.85% and a Word Error Rate (WER) of 26.83% on English handwritten text recognition, demonstrating competitive performance against representative methods. Ablation studies validate the contributions of both AC and SimRes. A case study on an aerospace equipment maintenance report further illustrates the component-level feasibility of the proposed workflow.