<p>This study presents methods for automatically detecting target texts in images through temporal and spatial alignment techniques. The temporal alignment is achieved by projecting each image onto its horizontal and vertical axes, followed by applying the dynamic time-warping algorithm to align the two projections independently. For spatial alignment, these projections are utilized to create an aligned image, from which the fixed rank kriging method is employed to extract the covariance structure of the aligned image. Both alignment procedures yield aligned feature sequences for each image. A compressed learning approach is then utilized to identify abnormal signals within these aligned feature sequences. Two specific applications are explored to demonstrate the effectiveness of the methods: handwritten traditional Chinese character classification and RoHS logo classification. The first application faces challenges due to the diverse shapes and styles of handwritten characters, while the second contends with background noise and varying forms of logos. Our numerical results demonstrate that the proposed methods consistently achieve superior or at least competitive identification performance compared with several widely used approaches, including convolutional neural networks, Tesseract optical character recognition (OCR), transformer OCR, EfficientNet-B5, and ResNet50, when the angles of text images are consistent.</p>