Early recognition of untrimmed handwritten gestures is the task of recognizing as soon as possible gestures drawn in a continuous stream, one after another. This is particularly challenging for multi-touch gestures because it is impossible to know when the gesture has started and finished. For mono-stroke gestures, in an application context where the finger is never removed from the device between gestures, the recognition is even more complex. In this work we present an extension of the Online Long-Term Convolutional 3D (OLT-C3D) network to address the task of early recognition of untrimmed gestures which have been addressed by very few works. To evaluate our approach, we created two synthetic datasets using freely available benchmarks, MTGSetB and ILGDB, simulating the streaming data in two different application scenarios. Furthermore, we propose a new evaluation metric for this specific task. Our approach achieves good performances on the two new datasets and will be a baseline for future works on this challenging task.