This research paper presents a unique Bengali OCR system with some
capabilities. The system excels in reconstructing document layouts while
preserving structure, alignment, and images. It incorporates advanced image and
signature detection for accurate extraction. Specialized models for word
segmentation cater to diverse document types, including computer-composed,
letterpress, typewriter, and handwritten documents. The system handles static
and dynamic handwritten inputs, recognizing various writing styles.
Furthermore, it has the ability to recognize compound characters in Bengali.
Extensive data collection efforts provide a diverse corpus, while advanced
technical components optimize character and word recognition. Additional
contributions include image, logo, signature and table recognition, perspective
correction, layout reconstruction, and a queuing module for efficient and
scalable processing. The system demonstrates outstanding performance in
efficient and accurate text extraction and analysis.

该研究提出了一种具有特殊能力的孟加拉语 OCR 系统，该系统在保留结构、对齐和图像的同时，在重建文档布局方面表现出色。它采用先进的图像和签名检测进行准确提取，并为文字分割提供了专门模型以适应多样化的文档类型，包括计算机排版、凸版印刷、打字机和手写文档。该系统处理静态和动态手写输入，识别各种写作风格。此外，它还能识别孟加拉语中的复合字符。广泛的数据收集工作提供了多样化的语料库，而高级技术组件优化了字符和单词识别。额外的贡献包括图像、标志、签名和表格识别，透视校正，布局重建以及用于高效和可扩展处理的排队模块。该系统在高效准确的文本提取和分析方面表现出色。

专用模型和高级技术提升孟加拉光学字符识别在多样文档类型中的应用

Enhancement of Bengali OCR by Specialized Models and Advanced Techniques  for Diverse Document Types

In the rapidly evolving digital era, the analysis of document layouts plays a
pivotal role in automated information extraction and interpretation. In our
work, we have trained MViTv2 transformer model architecture with cascaded mask
R-CNN on BaDLAD dataset to extract text box, paragraphs, images and tables from
a document. After training on 20365 document images for 36 epochs in a 3 phase
cycle, we achieved a training loss of 0.2125 and a mask loss of 0.19. Our work
extends beyond training, delving into the exploration of potential enhancement
avenues. We investigate the impact of rotation and flip augmentation, the
effectiveness of slicing input images pre-inference, the implications of
varying the resolution of the transformer backbone, and the potential of
employing a dual-pass inference to uncover missed text-boxes. Through these
explorations, we observe a spectrum of outcomes, where some modifications
result in tangible performance improvements, while others offer unique insights
for future endeavors.

在数字化快速发展的时代，文档布局的分析在自动化信息提取和解释中起着重要作用。通过在 BaDLAD 数据集上训练 MViTv2 转换器模型架构与级联掩膜 R-CNN，我们从文档中提取了文本框、段落、图像和表格。通过对 20365 个文档图像进行 36 个周期的训练，在 3 个阶段的循环中，我们取得了 0.2125 的训练损失和 0.19 的掩膜损失。同时，我们还探讨了旋转和翻转增强、在推理前对输入图像进行切片、改变转换器主干的分辨率以及使用双通道推理来发现遗漏的文本框等潜在的增强方法。通过这些探索，我们观察到一系列结果，一些修改带来了实质性的性能改进，而其他的修改为未来的努力提供了独特的见解。