  Text recognition is a major computer vision task with a big set of associated
challenges. One of those traditional challenges is the coupled nature of text
recognition and segmentation. This problem has been progressively solved over
the past decades, going from segmentation based recognition to segmentation
free approaches, which proved more accurate and much cheaper to annotate data
for. We take a step from segmentation-free single line recognition towards
segmentation-free multi-line / full page recognition. We propose a novel and
simple neural network module, termed \textbf{OrigamiNet}, that can augment any
CTC-trained, fully convolutional single line text recognizer, to convert it
into a multi-line version by providing the model with enough spatial capacity
to be able to properly collapse a 2D input signal into 1D without losing
information. Such modified networks can be trained using exactly their same
simple original procedure, and using only \textbf{unsegmented} image and text
pairs. We carry out a set of interpretability experiments that show that our
trained models learn an accurate implicit line segmentation. We achieve
state-of-the-art character error rate on both IAM \& ICDAR 2017 HTR benchmarks
for handwriting recognition, surpassing all other methods in the literature. On
IAM we even surpass single line methods that use accurate localization
information during training. Our code is available online at
\url{https://github.com/IntuitionMachines/OrigamiNet}.
