Handwritten character recognition (HCR) is a challenging learning problem in pattern recognition, mainly due to similarity in structure of characters, different handwriting styles, noisy datasets and a large variety of languages and scripts. HCR problem is studied extensively for a few decades but there is very limited research on script independent models. This is because of factors, like, diversity of scripts, focus of the most of conventional research efforts on handcrafted feature extraction techniques which are language/script specific and are not always available, and unavailability of public datasets and codes to reproduce the results. On the other hand, deep learning has witnessed huge success in different areas of pattern recognition, including HCR, and provides end-to-end learning, i.e., automated feature extraction and recognition. In this paper, we have proposed a novel deep learning architecture which exploits transfer learning and image-augmentation for end-to-end learning for script independent handwritten character recognition, called HCR-Net. The network is based on a novel transfer learning approach for HCR, where some of lower layers of a pre-trained VGG16 network are utilised. Due to transfer learning and image-augmentation, HCR-Net provides faster training, better performance and better generalisations. The experimental results on publicly available datasets of Bangla, Punjabi, Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu, Marathi, Nepali and Arabic languages prove the efficacy of HCR-Net and establishes several new benchmarks. For reproducibility of the results and for the advancements of the HCR research, complete code is publicly released at \href{https://github.com/jmdvinodjmd/HCR-Net}{GitHub}.
翻译:手写字符识别(HCR)在模式识别方面是一个具有挑战性的学习问题,主要原因是字符结构相似、笔迹风格不同、数据集杂乱以及多种语言和脚本。 HCR问题在几十年里得到了广泛研究,但对脚本独立模型的研究非常有限。这是因为各种因素,如剧本的多样性、大多数常规研究工作的重点放在手写特征提取技术上,这些技术是语言/脚本,并非总有,没有公共数据集和代码来复制结果。另一方面,深层次学习在模式识别的不同领域取得了巨大成功,包括HCR(HCR),并提供端到端学习,即自动特征提取和识别。在本文中,我们提出了一个全新的深层次学习结构,利用学习和图像推介,用于脚本独立的字符识别,称为 HCRR-Net(HCR-Net) 。这个网络基于一种新型的传输学习方法,一些经过训练的VGG16之前的阿拉伯语言网络的低层正在被设计成,并提供了端学习的HCRRRR(H) 和图像升级的更快的学习结果。