  Despite the advent of deep learning in computer vision, the general
handwriting recognition problem is far from solved. Most existing approaches
focus on handwriting datasets that have clearly written text and carefully
segmented labels. In this paper, we instead focus on learning handwritten
characters from maintenance logs, a constrained setting where data is very
limited and noisy. We break the problem into two consecutive stages of word
segmentation and word recognition respectively and utilize data augmentation
techniques to train both stages. Extensive comparisons with popular baselines
for scene-text detection and word recognition show that our system achieves a
lower error rate and is more suited to handle noisy and difficult documents
