We demonstrate an optical-camera-communication (OCC) system utilizing a laser-diode (LD) coupled optical-diffusing-fiber (ODF) transmitter (Tx) and rolling-shutter based image sensor receiver (Rx). The ODF is a glass optical fiber produced for decorative lighting or embedded into small areas where bulky optical sources cannot fit. Besides, decoding the high data rate rolling-shutter pattern from the thin ODF Tx is very challenging. Here, we propose and experimentally demonstrate the pixel-row-per-bit based neural-network (PPB-NN) to decode the rolling-shutter-pattern emitted by the thin ODF Tx. The proposed PPB-NN algorithm is discussed. The proposed PPB-NN method can satisfy the pre-forward error correction (FEC) BER at data rate of 3,300 bit/s at a transmission distance of 35 cm. Theoretical analysis of the maximum ODF Tx angle is also discussed; and our experimental values agree with our theoretical results.