Detecting Oriented Text in Natural Images by Linking Segments

Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line; A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512x512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.Comment: To Appear in CVPR 201

Rosetta: Large scale system for text detection and recognition in images

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.Comment: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 2018, London, United Kingdo

Silencing of D-Lactate Dehydrogenase Impedes Glyoxalase System and Leads to Methylglyoxal Accumulation and Growth Inhibition in Rice

D-Lactate is oxidized by two classes of D-lactate dehydrogenase (D-LDH), namely, NAD-dependent and NAD-independent D-LDHs. Little is known about the characteristics and biological functions of D-LDHs in rice. In this study, a functional NAD-independent D-LDH (LOC_Os07g06890) was identified in rice, as a result of alternative splicing events. Characterization of the expression profile, subcellular localization, and enzymatic properties of the functional OsD-LDH revealed that it is a mitochondrial cytochrome-c-dependent D-LDH with high affinity and catalytic efficiency. Functional analysis of OsD-LDH RNAi transgenic rice demonstrated that OsD-LDH participates in methylglyoxal metabolism by affecting the activity of the glyoxalase system and aldo-keto reductases. Under methylglyoxal treatment, silencing of OsD-LDH in rice resulted in the accumulation of methylglyoxal and D-lactate, the decrease of reduced glutathione in leaves, and ultimately severe growth inhibition. Moreover, the detached leaves of OsD-LDH RNAi plants were more sensitive to salt stress. However, the silencing of OsD-LDH did not affect the growth under photorespiration conditions. Our results provide new insights into the role of NAD-independent D-LDHs in rice