Despite growing global interest in information extraction from scanned documents, there is still a significant research gap concerning Nepali documents. This study seeks to address this gap by focusing on methods for extracting information from texts with Nepali typeface or Devanagari characters. The primary focus is on the performance of the Language Independent Layout Transformer (LiLT), which was employed as a token classifier to extract information from Nepali texts. LiLT achieved F1 score of approximately 0.87. Complementing this approach, large language models (LLMs), including OpenAI’s proprietary GPT-4o and the open-source Llama 3.1 8B, were also evaluated. The GPT-4o model exhibited promising performance, with an accuracy of around 55-80% accuracy for a complete match, accuracy varying among different fields. Llama 3.1 8B model achieved only 20-40% accuracy. For 90% match both GPT-4o and Llama 3.1 8B had higher accuracy by varying amounts for different fields. Llama 3.1 8B performed particularly poorly compared to the LiLT model. These results aim to provide a foundation for future work in the domain of digitization of Nepali documents.