The physical separation of perception and computation in von Neumann architectures necessitates extensive data movement between storage and processing units, resulting in high latency and energy consumption that constrain the development of edge vision applications. To reduce data transmission overhead and enhance front-end computational efficiency, this study proposes a hybrid recognition scheme based on optoelectronic memristor arrays and convolutional neural networks (CNN) for low-bandwidth feature acquisition and classification of 64×64 handwritten characters. Two sets of 4k optoelectronic memristor arrays perform parallel one-dimensional convolutional compression along row and column directions respectively, yielding two 64-dimensional feature streams that are concatenated into a 128-dimensional joint feature. A lightweight CNN is employed in the backend, combined with mixed-precision quantisation. Experimental results demonstrate that the combined row-column features achieve stable recognition rates of approximately 91%~93% without quantisation. Following quantisation and fine-tuning, the model attains a peak recognition rate of approximately 88.8%.