  Annotating words in a historical document image archive for word image
recognition purpose demands time and skilled human resource (like historians,
paleographers). In a real-life scenario, obtaining sample images for all
possible words is also not feasible. However, Zero-shot learning methods could
aptly be used to recognize unseen/out-of-lexicon words in such historical
document images. Based on previous state-of-the-art method for zero-shot word
recognition Pho(SC)Net, we propose a hybrid model based on the CTC framework
(Pho(SC)-CTC) that takes advantage of the rich features learned by Pho(SC)Net
followed by a connectionist temporal classification (CTC) framework to perform
the final classification. Encouraging results were obtained on two publicly
available historical document datasets and one synthetic handwritten dataset,
which justifies the efficacy of Pho(SC)-CTC and Pho(SC)Net.
