We consider the problem of handwritten text recognition (HTR) for collections of historical archive documents. The known HTR models can be split into two major categories – line-level models and page-level models. Line-level models for their training and inference require costly preprocessing procedures of careful extraction of image fragments corresponding to separate text lines. Page-level models do not require such preprocessing but usually show weaker recognition results. In this paper we propose a new HTR model YOLO-HTR that is aimed at closing a performance gap between line-level and page-level models. The new model simultaneously solves the tasks of detecting text lines and recognizing them, and thus does not require text line extraction for inference stage. The model architecture combines ideas from object detection YOLO model and HTR model Vertical Attention Network. In the paper we also propose a modification of CTC-loss that allows using text lines supervision with partially unknown text symbols – a common feature of expert supervision for challenging historical documents. Experiments were conducted on two collections of handwritten texts: the archive of diaries of the Russian navigator Fyodor Petrovich Litke and the archive of letters from prisoners of the Smolensk convict prison. The experiment results show that the proposed approach allows achieving recognition quality comparable to line-level models, with less labor costs.