In recent years many institutions holding papyri have put images of their collections online, while transcriptions previously published in print are now hosted in the Digital Corpus of Literary Papyri. Anagnosis aims to provide an intuitive and easy-to-use web interface between those images and related digitized texts. The main goal lies in automatic data processing and text-recognition accuracy: Through a dedicated OCR algorithm, letters on the image are identified with single boxes and thus linked to the transcription. A coordinates system of the glyphs on the image can then be transferred and applied to each new image uploaded for the same text section. Once all character boxes are generated, Anagnosis can extract a sample alphabet that users may rearrange to virtually restore lost parts of text directly on the image.

Encoding hieroglyphic texts is time-consuming. If a text already exists as hand-written transcription, there is an alternative, namely OCR. Off-the-shelf OCR systems seem difficult to adapt to the peculiarities of Ancient Egyptian. Presented is a proof-of-concept tool that was designed to digitize texts of Urkunden IV in the hand-writing of Kurt Sethe. It automatically recognizes signs and produces a normalized encoding, suitable for storage in a database, or for printing on a screen or on paper, requiring little manual correction. The encoding of hieroglyphic text is RES (Revised Encoding Scheme) rather than (common dialects of) MdC (Manuel de Codage). Earlier papers argued against MdC and in favour of RES for corpus development. Arguments in favour of RES include longevity of the encoding, as its semantics are font-independent. The present study provides evidence that RES is also much preferable to MdC in the context of OCR. With a well-understood parsing technique, relative positioning of scanned signs can be straightforwardly mapped to suitable primitives of the encoding.

In recent years many institutions holding papyri have put images of their collections online, while transcriptions previously published in print are now hosted in the Digital Corpus of Literary Papyri. Anagnosis aims to provide an intuitive and easy-to-use web interface between those images and related digitized texts. The main goal lies in automatic data processing and text-recognition accuracy: Through a dedicated OCR algorithm, letters on the image are identified with single boxes and thus linked to the transcription. A coordinates system of the glyphs on the image can then be transferred and applied to each new image uploaded for the same text section. Once all character boxes are generated, Anagnosis can extract a sample alphabet that users may rearrange to virtually restore lost parts of text directly on the image.

Encoding hieroglyphic texts is time-consuming. If a text already exists as hand-written transcription, there is an alternative, namely OCR. Off-the-shelf OCR systems seem difficult to adapt to the peculiarities of Ancient Egyptian. Presented is a proof-of-concept tool that was designed to digitize texts of Urkunden IV in the hand-writing of Kurt Sethe. It automatically recognizes signs and produces a normalized encoding, suitable for storage in a database, or for printing on a screen or on paper, requiring little manual correction. The encoding of hieroglyphic text is RES (Revised Encoding Scheme) rather than (common dialects of) MdC (Manuel de Codage). Earlier papers argued against MdC and in favour of RES for corpus development. Arguments in favour of RES include longevity of the encoding, as its semantics are font-independent. The present study provides evidence that RES is also much preferable to MdC in the context of OCR. With a well-understood parsing technique, relative positioning of scanned signs can be straightforwardly mapped to suitable primitives of the encoding.