A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SBMNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.

In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective. The ACE loss function exhibits competitive performance to CTC and the attention mechanism, with much quicker implementation (as it involves only four fundamental formulas), faster inference\back-propagation (approximately O(1) in parallel), less storage requirement (no parameter and negligible runtime memory), and convenient employment (by replacing CTC with ACE). Furthermore, the proposed ACE loss function exhibits two noteworthy properties: (1) it can be directly applied for 2D prediction by flattening the 2D prediction into 1D prediction as the input and (2) it requires only characters and their numbers in the sequence annotation for supervision, which allows it to advance beyond sequence recognition, e.g., counting problem. The code is publicly available at https://github.com/summerlvsong/Aggregation-Cross-Entropy.

To date, offline handwriting paragraph recognition systems either separately crop text line images and recognize them or perform implicit line segmentation by integrating complicated multi-dimensional long short-term memory (MDLSTM) with an attention mechanism. The former abovementioned approachs could lead to sub-optimal performances while the latter is very time-consuming. In this paper, a fast end-to-end system, called adversarial feature enhancing network (AFEN), is proposed for offline handwritten paragraph recognition. The proposed AFEN system comprises five components: a shared feature extractor for robust feature learning, a text detection branch for text box proposal; RoIRotate for oriented feature region extraction, an adversarial feature learning network for joint feature learning of text detection and recognition branch, and a text recognition branch for text transcription. Experiments on two popular handwritten paragraph recognition benchmarks, namely IAM and Rimes are used to verify the efficacy of the proposed AFEN system. The proposed approach yields impressive results compared to previously proposed systems in the literature.

With the passing of history, precious cultural heritage was left behind to tell ancient stories, especially those in the form of written documents. In this paper, a weakly supervised segmentation system with recognition-guided information on attention area, is proposed for high-precision historical document segmentation under strict intersection-over-union (IoU) requirements. We formulate the character segmentation problem from Bayesian decision theory perspective and propose boundary box segmentation (BBS), recognition-guided BBS (Rg-BBS), and recognition-guided attention BBS (Rg-ABBS), progressively, to search for the segmentation path. Furthermore, a novel judgment gate mechanism is proposed to train a high-performance character recognizer in an incremental weakly supervised learning manner. The proposed Rg-ABBS method is shown to substantially reduce time consumption while maintaining sufficiently high precision of the segmentation result by incorporating both character recognition knowledge and line-level annotation. Experiments show that the proposed Rg-ABBS system significantly outperforms traditional segmentation methods as well as deep-learning-based instance segmentation and detection methods under strict IoU requirements.

As an important prerequisite step of historical document image analysis, character segmentation is fundamental but challenging. In this paper, we propose a novel approach for the handwritten character segmentation of historical documents by treating it as a sequence labeling problem. In more detail, the proposed model first segments document image into lines, then each column in the line image is given a label to indicate it is a segmentation position or not. The segmentation labeling is achieved by a neural model, which combines a CNN for feature extraction, a LSTM for sequence modeling and a CRF for sequence labeling. The performance of our methods has been evaluated on a 300-page dataset including 96,479 characters. The experimental results demonstrate that the proposed methods achieve superior or highly competitive performance compared with other methods.

Handwritten texts with various styles, such as horizontal, overlapping, vertical, and multi-lines texts, are commonly observed in the community. However, most existing handwriting recognition methods only concentrate on one specific kind of text style. In this paper, we focus on the problem of new unconstrained handwritten text recognition and propose distilling gated recurrent unit (GRU) with a new data augmentation technology to model the complex sequential dynamic of unconstrained handwriting text of various styles. The proposed data augmentation method can synthesize realistic handwritten text datasets including horizontal, vertical, overlap, right-down, screw-rotation, and multi-line situation, which render our framework robust for general purposes. The recommended distilling GRU can not only accelerate the training speed through the distilling stage but also maintain the original recognition accuracy. Experiments on our synthesized handwritten test sets show that the proposed multi-layer GRU performs well on the unconstrained handwriting text recognition problem. On the ICDAR2013 handwritten text recognition benchmark dataset, the proposed framework demonstrates comparable performance with state-of-the-art techniques.