Word spotting in Khmer printed documents presents a unique challenge due to the complexities of the Khmer script and the vast array of font styles employed. The scarcity of large, publicly available datasets further complicates this task. This work proposes a two-module approach for achieving accurate and efficient word spotting in Khmer documents. Separate datasets are utilized for text detection and recognition. The first module employs the state-of-the-art YOLOv8 model on a dataset of 10,050 text samples. The model's performance is evaluated using the F1 score, a metric that balances precision and recall in locating text. The second module leverages the fine-tuned Transformer-based TrOCR model for recognition, trained on 22,567 labeled words, with recognition accuracy measured by the Character Error Rate (CER). The first module achieves an impressive F1 score of 0.987 in locating Khmer words within documents. The second module's TrOCR model results in a CER of 8.41%. By overcoming script and font challenges through focused datasets and advanced models, this approach demonstrates potential for improving document processing and information retrieval for the Khmer language.