BanglaWriting: A multi-purpose offline Bangla handwriting dataset

LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition

Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla. The complexity arises from the existence of a large number of alphabets, the presence of several diacritic forms, and the appearance of complex conjuncts. The difficulty is exacerbated by the fact that some graphemes occur infrequently but remain indispensable, so addressing the class imbalance is required for satisfactory results. This paper addresses this issue by introducing two knowledge distillation methods: Leveraging Isolated Letter Accumulations By Ordering Tea…

MatriVasha：一个用于孟加拉语手写复合字符的多用途综合数据库

MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

本文构建MatriVasha数据集，收录来自孟加拉国不同地区、年龄与性别书写者的120类共2552个孟加拉语手写复合字符，以弥补综合数据集缺失并支持识别研究。

BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation

We introduce a new dataset for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant to act as ground truth texts. These texts were subsequently used to generate the annotations that were filled out by people with their handwriting. Our dataset includes 788 images of handwritten pages produced by approximately 150 different writers. It can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spo…

BN-DRISHTI：通过手写文本图像的实例级分割实现孟加拉语文档识别

BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images

提出BN-DRISHTI，结合YOLO、Hough变换与仿射校正完成孟加拉语手写文本实例分割，并扩展BN-HTRd数据集，在多个外部基准上显著领先。

A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition

Bangla Handwritten Digit recognition is a significant step forward in the development of Bangla OCR. However, intricate shape, structural likeness and distinctive composition style of Bangla digits makes it relatively challenging to distinguish. Thus, in this paper, we benchmarked four rigorous classifiers to recognize Bangla Handwritten Digit: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) based on three handcrafted feature extraction techniques: Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Gabor…

从转录的孟加拉语文本中进行开放词汇单词识别

Open Vocabulary Word Recognition From Transcribed Bangla Texts

本研究利用SSD、Faster R-CNN及集成模型识别手写孟加拉语单词，改进非极大值抑制，在9841图像数据集上集成模型F1达92.61%，单词识别率96.12%。