Продвинутая архитектура на базе двунаправленных LSTM-сетей, созданная специально для распознавания рукописного ввода. Благодаря функции потерь CTC, эта ИИ-модель мастерски справляется с расшифровкой текста, написанного от руки на досках или бумаге.
In this paper we introduce a new connectionist approach to on-line handwriting recognition and address in particular the problem of recognizing handwritten whiteboard notes. The approach uses a bidirectional recurrent neural network with the long short-term memory architecture. We use a recently introduced objective function, known as Connectionist Temporal Classification (CTC), that directly trains the network to label unsegmented sequence data. Our new system achieves a word recognition rate of 74.0 %, compared with 65.4 % using a previously developed HMMbased recognition system.