Automatic Synthesis of Historical Arabic Text for Word-Spotting

Abstract

We present a novel framework for automatic and efficient synthesis of historical handwritten Arabic text. The main purpose of this framework is to assist word spotting and keyword searching in handwritten historical documents. The proposed framework consists of two main procedures: building a letter connectivity map and synthesizing words. A letter connectivity map includes multiple instances of the various shape of each letter, since a letter in Arabic usually has multiple shapes depends in its position in the word. Each map represents one writer and encodes the specific handwriting style. The letter connectivity map is used to guide the synthesis of any Arabic continuous subword, word, or sentence. The proposed framework automatically generates the letter connectivity map annotation from a several pages historical pages previously annotated. Once the letter connectivity map is available our framework can synthesis the pictorial representation of any Arabic word or sentence from their text representation. The writing style of the synthesized text resembles the writing style of the input pages. The synthesized words can be used in word-spotting and many other historical document processing applications. The proposed approach provides an intuitive and easy-to-use framework to search for a keyword in the rest of the manuscript. Our experimental study shows that our approach enables accurate results in word spotting algorithms.

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

“…There are very few Arabic handwriting keyword spotting studies in the literature [6]. Some QbE studies ( [18][19][20]) are proposed for the historical Arabic documents and used a matching method adjusted to the Arabic script. QbS approaches [21,22] used the HMM technique for keyword spotting in handwritten Arabic manuscripts.…”