Deepcheque: a Large Language Model Approach to Automated Cheque Collection Framework and Information Retrival Using Multiple Loss Functions

The field of automated cheque collection has received a lot of attention recently due to the complexity and time-consuming nature of the manual process, which requires significant investment in technology, infrastructure, and staff training. However, some key areas such as Customer Experience and Interoperability with Image Processing and machine learning systems have not been explored. The challenge lies in the fact that handwritten characters are unique, and there is a lack of open-sourced foundation models. Furthermore, cheques and bank receipts contain both handwritten and non-handwritten text, making it difficult to extract information. The research proposes a novel framework that takes curvature into account when learning and employs CNN LSTM and Yolov5-OCR to extract information from printed and handwritten documents. The CTC-loss-function has limited application to processing bank cheques we had identified this gap and further improved overfitting which results due to CTC loss by combining CTC, curvature loss, and embedding loss, This research has further contributed to the development of an automated cheque collection framework that can extract information from both printed and handwritten texts, improving the overall efficiency of the cheque collection process and interoperability within various systems which is laborious in nature.