AI Archives Risk Erasing What Holocaust Last Writings Most Need to Say

In the autumn of 1944, the Hungarian poet Miklós Radnóti was marched westward from a forced labor camp in Serbia and shot near the village of Abda. When his mass grave was opened in 1946, a small notebook was recovered from his clothing, soaked and blackened by wet earth. Its final poem, “Razglednica (4),” was not written on a clean page but across the back of a rust-colored advertisement for cod liver oil, complete with the image of an X-rayed hand and the promise of medicine. Over that compromised surface, Radnóti wrote his last lines about patience flowering into death and dark, filthy blood drying on his ear. The poem’s power, a new study argues, does not reside in the words alone, nor even in the forensic recovery of the notebook from his corpse. It emerges from the unstable relation between the death march, the verbal form, the repurposed advertisement, and the nearly impossible route by which the object reached an archive.

That insight anchors a study published in the journal AI & Society by Yael S. Hacohen of Tel Aviv University, who introduces the concept of “material uncertainty”: the condition of fragile textual objects whose meaning cannot be separated from the damaged, improvised, or unstable material forms through which they survive. Her argument targets a quiet but consequential problem in modern archives. Handwritten-text recognition, optical character recognition, automated translation, metadata generation, and pattern detection now promise to make dispersed, multilingual collections searchable at scale. These tools are no longer peripheral experiments; they are part of the ordinary infrastructure of archival work. Yet the very features that make Holocaust last writings historically and ethically charged—stains, creases, improvised surfaces, uncertain addresses, broken chains of custody—are precisely the features least easily stabilized as data.

Hacohen reconstructs the standard computational pipeline in technical detail. Preprocessing begins after image acquisition with binarization, deskewing, noise reduction, and contrast enhancement, operations that establish the categories through which an image will be read: text, background, distortion, noise. In the humanities scholar Johanna Drucker’s terms, data are produced rather than discovered—”capta,” actively taken rather than given. Segmentation then divides the page into processable spatial units such as text regions, columns, lines, and baselines. Processing converts visual marks into machine-readable characters, with modern handwriting-recognition systems relying on deep-learning architectures, including recurrent neural networks and encoder–decoder models with attention mechanisms, to infer probable words despite ambiguous letterforms. Post-processing follows: outputs are cleaned and normalized, named entities are tagged into searchable metadata fields, neural machine translation trained on historical corpora mediates linguistic access, and classification models sort documents by subject, genre, period, and provenance.

Each stage is genuinely valuable, and the study does not pretend otherwise. Machine learning has enabled large-scale transcription of handwritten records that would otherwise remain unsearchable, and it can recover text through stains, tears, and fading. But the pipeline privileges what can be stabilized—names, dates, standardized categories, recurring linguistic patterns—while whatever cannot be confidently parsed becomes “error” and whatever cannot be standardized becomes “noise.” Those residual categories, as classification scholars Geoffrey Bowker and Susan Leigh Star showed, are where systems accumulate their exclusions. The greater danger is not error but displacement: once an object has been scanned, segmented, transcribed, and indexed, the clean derivative may begin to circulate as though it had exhausted the damaged artifact from which it was derived. To test where that displacement occurs, Hacohen executed a diagnostic workflow trace on three Holocaust last writings chosen as materially heterogeneous limit cases.

The first is Eva Schulzová’s illustrated poem “One Night in Terezín.” Deported to Theresienstadt at the age of ten in December 1941, Schulzová composed the pencil-on-paper work two years later, intertwining realistic drawings of the Dresden barracks with a tercet and seven couplets, moving from the armed gendarme outside to imprisoned women whispering tales of home, and finally to a faintly penciled sketch of a lavish feast—roasted turkey and cake—beside the question of how many more nights they will endure, an answer she notes only God knows. Less than a month later she was deported to Auschwitz-Birkenau, where she was murdered. The workflow often succeeded technically: it recovered much of the verbal sequence and produced a richer searchable record. But background normalization treated yellowing and fold shadow as interference, thresholding forced a page of continuous tonal variation into foreground and background, and segmentation could not determine whether pencil was functioning as script, image, interruption, or scene—a distinction the manuscript itself does not sustain.

The metadata stage made the loss vivid. The system categorized the document as a handwritten lyric poem in rhyming couplets, recorded “illustrations: true,” and described the drawings as a guard booth, clock, window, bed scene, and “food still-life.” It captured the presence of drawings but not a single relation between words and images, eliminating the object as multimodal entirely. It misidentified a guarded prisoner hut as a guard booth, overlooked the sunset entirely, and reduced a grieving child’s imagined feast to a still-life label. The handwriting model, meanwhile, recovered much of the Czech text but its errors clustered around the very words that fix the poem to Terezín—the ghetto, the Dresden Barracks, the term for Jews—resolving uncertain words into plausible Czech at precisely the points that anchor the poem historically, without marking those readings as inference rather than recovery. The faint final couplet, where the child’s address becomes most unstable, was also where the material trace was least secure; and the metadata recorded the poem’s archival location without registering the complete absence of knowledge about how it survived.

The second limit case, Eliezer Heiman’s two inscribed clay tablets, exposes a categorical failure. A Hebrew and Yiddish writer hidden with his family during the liquidation of the Kovno ghetto, Heiman engraved his final words on fired clay on September 22, 1943, deliberately recalling the Tablets of the Law: the right tablet records the names of those concealed in “the house of slaves and the house of doom,” while the left attests that he wrote the columns “with my own two hands” as “a memorial for generations to come.” The tablets were recovered after the hiding place was bombed and burned in July 1944. Computationally, the object defeated the pipeline almost immediately. Because Heiman’s letters are incised and made visible through depth, shadow, and ochre infill—now partially flaked—the flaked patches share the scale, color, and contrast of the engraved strokes. Adaptive thresholding classified roughly a third of the clay surface as foreground; line detection collapsed because flaking filled the interlinear spaces. The inscription was not hidden behind removable noise; it survives through a damaged surface materially entangled with the writing. The study distinguishes this as a capture-level loss, fixed at acquisition, and recommends reflectance transformation imaging or photometric stereo to record surface geometry rather than flattening relief into a single intensity image.

The third case, Sal Bramson’s cloth fragment, shows how partial success can be the most insidious outcome. Arrested with his brother in Amsterdam in 1942, Bramson produced drawings and written messages on white cotton cloth during nine months in an Amsterdam prison, smuggling them to his mother hidden in a shirt collar through the weekly laundry circuit. The surviving fragment holds four numbered drawings of his cell with handwritten Dutch captions of architectural precision—dimensions, sightlines, the peephole, the one hour of hot food. The workflow could mask the cloth, suppress the periodic weave, and binarize strokes with reasonable confidence, but suppressing the weave weakens the evidence of the very medium that made the message possible, since the cloth could travel only because it was foldable, concealable cloth. Pixel classification separated pictorial from textual components, yet caption and drawing are one documentary apparatus: the caption guides the mother’s eye while the drawing gives it spatial force. The workflow separated as layout what functions as address, and lexical correction risked converting fragmented handwriting into plausible Dutch that was grammatically smoother but historically wrong. The brothers were deported to Sobibor on April 27, 1943, and murdered three days later.

From these traces Hacohen draws a design imperative rather than a rejection of computation. The remedy is a relation-preserving workflow instead of an extraction-driven one: one that keeps the original image, processed image, transcript, uncertain readings, translation, material annotations, and provenance notes in view at once; that documents what was enhanced, suppressed, inferred, and left undecidable; and that marks inference as inference, as when an early machine reading placed Heiman’s tablets in Vilna rather than Viliampolė and was corrected only by re-examination under magnification. The ethical demand, the study concludes, is not that uncertainty be romanticized—transcription, translation, and clarification remain necessary—but that clarification remain answerable to the damaged form through which the writing survived. The faintness of Schulzová’s final couplet, the depth and soot of Heiman’s clay, the crowded textile of Bramson’s cloth, and the advertisement beneath Radnóti’s last poem are not obstacles to the evidence. They are part of it, and the future of archival computation should be measured not only by what it can recover, but by whether it can preserve the uncertainty through which these last writings continue to speak.

Subject of Research: How computational archival workflows transform Holocaust last writings into machine-readable data and what is lost in that transformation.

Article Title: Material uncertainty in computational workflow in the archives: Holocaust last writings beyond machine-readable extraction

Article References: Material uncertainty in computational workflow in the archives: Holocaust last writings beyond machine-readable extraction. (n.d.). https://doi.org/10.1007/s00146-026-03378-y

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03378-y

Keywords: material uncertainty, computational archives, Holocaust last writings, handwritten text recognition, digital humanities, machine-readable archives, archival materiality, OCR, HTR, cultural heritage, AI ethics, Material