Background/Objectives: Today, the deep neural network has more intermediate layers along with learning data and has gradually become larger to draw a lower result due to the bound conventional neural network. Methods/Statistical analysis: Therefore, in this paper, we proposed the line-segment feature extraction for input dimensionality reduction in pattern mining. The proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using predefined filters. Using such unique values to identify the number of line segments, create a one-dimensional vector, in the size of 256 or 512. Findings: This vector is used as the input data for a multi-layer perceptron (MLP). For performance evaluation, LFA was compared with principal component analysis (PCA). As a result, LFA-256 (at 96.81% accuracy) had −0.1% lower performance but a faster speed than PCA (96.91% accuracy), and LFA-512 (at 97.25% accuracy) had 0.35% higher performance than PCA. Improvements/Applications: We will study the recognition service possible to use in industry (mobile devices, PC, edge computing, etc.) and real-life through this algorithm.