Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
This addresses the problem of recognizing diverse text types (e.g., handwriting, scene text) for computer vision applications, with incremental improvements in efficiency and accuracy.
The authors tackled unconstrained text recognition by proposing a data-efficient, end-to-end convolutional neural network without recurrent connections, achieving state-of-the-art results on seven benchmark datasets and winning the ICFHR2018 competition.
Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.