Work

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Year: 2017

Type: conference-paper

Abstract: We introduce an algorithm for word-level text spotting that is able to accurately and reliably determine the bounding regions of individual words of text "in the wild". Our system is formed by the cascade of two convolutional neural networks. The first network is fully convolutional and is in charge of detecting areas containing text. This results in a very reliable but possibly inaccurate segmentation of the input image. The second network (inspired by the popular YOLO architecture) analyzes each segment produced in the first stage, and predicts oriented rectangular regions containing individual words. No post-processing (e.g. text line grouping) is necessary. With execution time of 450 ms for a 1000 × 560 image on a Titan X GPU, our system achieves good performance on the ICDAR 2013, 2015 benchmarks [2], [1].

Source: IAPR International Conference on Document Analysis and Recognition (ICDAR)

DOI: 10.1109/icdar.2017.210

Authors: Siyang Qin, Roberto Manduchi

Institution: University of California, Santa Cruz

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Eye Institute

Award: Enabling access to printed text for blind people via assisted mobile OCR