historical-document-analysis

Implementation of PHOSNet and Pho(SC)Net for Word Recognition in Historical Documents. Implemented using Tensorflow 2.x

📜 [ICDAR 2021] "A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts", S P Sharan, Sowmya Aitha, Amandeep Kumar, Abhishek Trivedi, Aaron Augustine, Ravi Kiran Sarvadevabhatla

Semantic Segmentation of Historical Documents using Deep Learning Architectures

SegClarity: An attribution-based XAI workflow for layer-wise interpretability in semantic segmentation

The largest publicly released line-level dataset of historical Arabic manuscripts — 14 books, 3,043 pages, 28,600 lines, with margin/insertion-anchor annotations for non-linear reading order.

Code and supplementary results for the paper "S. M. Unter, E. L. Hertel, DDD - A Diagnostic Dataset for Character Recognition and Detection on Ancient Egyptian Hieratic Characters and Words" at the ICDAR 2026 conference.

An entire model pipeline of data ingestion, model training, evaluation, and segment management into a single CLI application for the Vesuvius Challenge.