Despite being studied extensively for a few decades, handwritten character recognition (HCR) is still considered a challenging learning problem in pattern recognition, and there is very limited research on script independent models. This is mainly because of similarity in structure of characters, different handwriting styles, noisy datasets, diversity of scripts, focus of the conventional research on handcrafted feature extraction techniques, and unavailability of public datasets and code-repositories to reproduce the results. On the other hand, deep learning has witnessed huge success in different areas of pattern recognition, including HCR, and provides an end-to-end learning. However, deep learning techniques are computationally expensive, need large amount of data for training and have been developed for specific scripts only. To address the above limitations, we have proposed a novel generic deep learning architecture for script independent handwritten character recognition, called HCR-Net. HCR-Net is based on a novel transfer learning approach for HCR, which partly utilizes feature extraction layers of a pre-trained network. Due to transfer learning and image-augmentation, HCR-Net provides faster and computationally efficient training, better performance and better generalizations, and can work with small datasets. HCR-Net is extensively evaluated on 40 publicly available datasets of Bangla, Punjabi, Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu, Marathi, Nepali and Arabic languages, and established 26 new benchmark results while performed close to the best results in the rest cases. HCR-Net showed performance improvements up to 11% against the existing results and achieved a fast convergence rate showing up to 99% of final performance in the very first epoch. HCR-Net significantly outperformed the state-of-the-art transfer learning techniques...

翻译：尽管手写字符识别（HCR）已被广泛研究数十年，但在模式识别领域仍被视为一个具有挑战性的学习问题，且针对脚本无关模型的研究十分有限。这主要源于字符结构的相似性、不同书写风格、噪声数据集、脚本多样性、传统研究对人工特征提取技术的侧重，以及公共数据集和代码库的匮乏导致结果难以复现。另一方面，深度学习在包括HCR在内的不同模式识别领域取得了巨大成功，并提供了端到端学习。然而，深度学习技术计算成本高、需要大量训练数据，且仅针对特定脚本开发。为解决上述局限性，我们提出了一种新颖的通用深度学习架构——HCR-Net，用于脚本无关的手写字符识别。HCR-Net基于一种新颖的HCR迁移学习方法，部分利用了预训练网络的特征提取层。由于迁移学习和图像增强，HCR-Net提供了更快速且计算高效的训练、更优的性能和更好的泛化能力，并能处理小数据集。HCR-Net在40个孟加拉语、旁遮普语、印地语、英语、瑞典语、乌尔都语、波斯语、藏语、卡纳达语、马拉雅拉姆语、泰卢固语、马拉地语、尼泊尔语和阿拉伯语的公开数据集上进行了广泛评估，建立了26个新的基准结果，并在其余案例中取得了接近最优的性能。与现有结果相比，HCR-Net的性能提升高达11%，且实现了快速收敛，在第一个训练周期内即达到最终性能的99%。HCR-Net显著优于最先进的迁移学习技术...