Custom Character Recognition Algorithmic Framework for Image Processing on a Raspberry Pi 4:

Given the context of deploying image processing workflows on a Raspberry Pi 4 – a micro-computational entity with constraints pertaining to computational throughput and memory bandwidth – the feasibility of crafting a bespoke character recognition algorithm not only becomes an intellectual pursuit but also a practical exigency.

1. Convolutional Neural Network (CNN) Topologies: Leveraging the potency of CNNs, which have manifested preeminence in the domain of computer vision due to their adeptness at extracting hierarchical spatial features from imagery, can be the cornerstone of your endeavor. These topologies employ convolutional kernels to scrutinize local features, facilitating an intricate understanding of spatial hierarchies in the visual spectrum.

2. Transfer Learning & Pre-trained Models: Owing to the computational constraints of the Raspberry Pi infrastructure, it's sagacious to contemplate the utilization of transfer learning. By harnessing pre-trained models, such as VGG16 or ResNet, which have been erstwhile trained on prodigious datasets like ImageNet, and fine-tuning them on a niche dataset of vehicle registration characters, you can circumvent the computational trudgery of training from scratch.

3. Optical Character Recognition (OCR) Stratagems: Traditional OCR techniques, hinging on contour detection, thresholding, and morphological transformations, can be amalgamated with machine learning classifiers, such as Support Vector Machines (SVM) or Random Forests. These hybrid paradigms can be tailored for scenarios wherein deep learning architectures might impose undue computational burdens.

4. Temporal Data Annotation: Once character recognition is effectuated, it's imperative to synergize it with a temporal data acquisition module. Python libraries like datetime can be employed to effectuate real-time timestamping, thereby ensuring the chronological integrity of your data as it's uploaded to your real-time database.

5. Database Synchronization & Real-time Data Ingestion: Utilizing robust middleware, such as MQTT or Node-RED, can streamline the process of transmuting your character-recognized and time-stamped data into real-time database ecosystems. Whether you're gravitating towards relational databases or NoSQL data lakes, these tools can expedite data ingestion and ensure data fidelity.

In summation, while tools like easy-ocr offer a convenient out-of-the-box solution, the exigencies of your specific deployment milieu, particularly the Raspberry Pi infrastructure, necessitate a more bespoke and nuanced approach. Crafting a proprietary character recognition algorithm amalgamating both classical computer vision techniques and contemporary deep learning stratagems stands as not only a viable but an intellectually rewarding trajectory.