Flash memory is essential for storing vital data, including firmware, user information, and cryptographic secrets, in modern electronic devices. This makes data recovery from flash memories critical, particularly when existing non-invasive methods are inadequate. In this talk, we share an invasive Deep-Learning-based Data Recovery (DLDR) framework designed to recover binary bits at the memory cell level and reorganize them into word-level data. Our proposed DLDR framework employs deep learning models to recover bit values from microscopic images of the flash memory chip in the target device. By using a profiling device with similar circuitry, our framework obtains address and bit order information, enabling the reconstruction of recovered bits into word-level data with corresponding addresses. Experiments on an 8-bit microcontroller with 16KB flash memory demonstrate the effectiveness of our proposed DLDR framework.