<p>Answer sheet layout analysis is an important preliminary task for handwritten answer sheet recognition. Existing layout analysis methods can analyze the handwritten content of students in the handwritten answer sheet to the level of text lines. However, because of the presence of various elements such as text, mathematical expressions, and systems of equations within the answer sheet text lines, their content is heterogeneous and structurally complex, making direct recognition impossible. To address this challenge, we propose an efficient multi-scale bidirectional parallel feature fusion network (EMBFN). This model enables the analysis of handwritten answer sheet text lines and optimizes feature fusion by enhancing the representations extracted by the backbone network. It mitigates feature conflicts arising from interlayer discrepancies during fusion, thereby reducing errors in sparse boundary analysis and the misclassification of similar elements. EMBFN uses a dynamic competitive fusion block to address detail ambiguity and semantic conflicts arising from static fusion strategies, thereby enhancing the effectiveness of feature fusion. In addition, we constructed an Answer Sheet Text Line Analysis (ASTA) dataset to complement missing analysis elements, such as logical symbols and smudges, in existing public datasets. It contains handwritten lines of text from a variety of disciplines and annotates all elements within the lines of text. Experimental results on both the ASTA dataset and publicly available datasets demonstrate the effectiveness of our proposed method. Notably, our network model contains only 2.2 million parameters, while achieving an accuracy of 96.7%.</p>