SCUT-HCCDoc: A new benchmark dataset of handwritten Chinese text in unconstrained camera-captured documents. (December 2020) - Zhang, Hesuo; Liang, Lingyu; Jin, Lianwen
Highlights: We propose a new large-scale SCUT-HCCDoc dataset for handwritten Chinese text detection, recognition and spotting in natural images. We statistically analyzed the samples and annotations of SCUT- HCCDoc in image level, text level and character level. We use state-of-the-art methods for baseline evaluation of text line detection, text line recognition, and end-to-end text spotting. A comprehensive analysis of the challenge of the dataset and benchmark testing are provided. Abstract: In this paper, we introduce a large-scale dataset, called SCUT-HCCDoc, to address challenging detection and recognition problems of handwritten Chinese text (HCT) in the camera-captured documents. Despite extensive studies of optical character recognition (OCR) and offline handwriting recognition for document images, text detection and recognition in the camera-captured documents remains an unsolved problem that is worth for extensive study and investigation. With recent advances in deep learning, researchers have proposed useful architectures for feature learning, detection, and recognition for the scene text. However, the performance of deep learning methods highly depends on the amount and diversity of training data. Previous OCR and offline HCT datasets were built under specific constraints, and most of the recent scene text datasets are for non-handwritten text. Hence, there is a lack of a comprehensive scene handwritten text benchmark. This study focuses on scenes with handwritten Chinese text. We introduce the SCUT-HCCDoc database for HCT detection, recognition and spotting. SCUT-HCCDoc contains 12, 253 camera-captured document images with 116, 629 text lines and 1, 155, 801 characters. The diversity of SCUT-HCCDoc can be described at three levels: (1) image-level diversity : image appearance and geometric variances caused by camera-captured settings (such as perspective, background, and resolution) and different applications (such as note-taking, test papers, and homework); (2) text-level diversity : variances of text line length, rotation, etc.; (3) character-level diversity : variances of character categories (up to 6109 classes with additional English letters, and digits), character size, individual writing style, etc. Three kinds of baseline experiments were conducted, where we used several popular text detection methods for text line detection, CTC-based/attention-based methods for text line recognition, and combine text detectors with CTC-based recognizer to achieve end-to-end text spotting. The results indicate the diversity of SCUT-HCCDoc and the challenges of HCT understanding in document images. The dataset is available at https://github.com/HCIILAB/SCUT-HCCDoc_Dataset_Release .