Vast collections of documents available in image format need to be indexed for information
retrieval purposes. In this framework, word spotting is an alternative solution to optical …

Natural history collections (NHCs) are the foundation of historical baselines for assessing
anthropogenic impacts on biodiversity. Along these lines, the online mobilization of …

The success of deep learning based models have centered around recent architectures and
the availability of large scale annotated data. In this work, we explore these two factors …

We introduce two data augmentation and normalization techniques, which, used with a CNN-
LSTM, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond …

The recognition of handwritten text is challenging as there are virtually infinite ways a human
can write the same message. Deep learning approaches for handwriting analysis have …

Providing computers with the ability to process handwriting is both important and
challenging, since many difficulties (eg, different writing styles, alphabets, languages, etc.) …

Abstract This paper proposes Convolve, Attend and Spell, an attention-based sequence-to-
sequence model for handwritten word recognition. The proposed architecture has three …

Deep convolutional features for word images and textual embedding schemes have shown
great success in word spotting. In this work, we follow these motivations to propose an …

We present a framework for learning an efficient holistic representation for handwritten word
images. The proposed method uses a deep convolutional neural network with traditional …

Abstract Handwritten Word Recognition and Spotting is a challenging field dealing with
handwritten text possessing irregular and complex shapes. The design of deep neural …