<![CDATA[Automation of data extraction in financial documents is an urgent necessity for MYO Adventures to mitigate the risks of manual entry errors and operational inefficiencies. This study aims to evaluate the performance of Deep Computer Vision modules within an automated validation system through a comparative analysis between Connectionist Text Proposal Network (CTPN) and Efficient and Accurate Scene Text Detector (EAST) algorithms. The research methodology employs a quantitative experimental approach, testing both models against invoice and receipt datasets characterized by physical distortions and dense tabular layouts. Evaluation focuses on inference time metrics and text localization effectiveness prior to the Optical Character Recognition (OCR) stage. The results reveal a significant performance disparity, where the EAST algorithm recorded an average detection time of 565 ms, substantially more efficient than CTPN which required an extreme 12,046 ms. In terms of coverage accuracy, EAST demonstrated high robustness by successfully recognizing 74 valid text items, in contrast to CTPN which yielded only 14 readable items. The poor performance of CTPN is analyzed as a consequence of the limitations of its vertical anchor mechanism and Recurrent Neural Network (RNN) in handling the dense spacing variations of invoice tables, whereas the Fully Convolutional Network (FCN) architecture of EAST proved to be more adaptive. In conclusion, anchor-free methods like EAST possess superior reliability and computational efficiency, making them the most viable solution for implementing real-time financial validation systems on MYO Adventures' web-based platform.]]>
