fix(server): use bigrams for cjk

server/src/repositories/ocr.repository.ts

@@ -45,12 +45,12 @@ export class OcrRepository {
           textScore: DummyValue.NUMBER,
         },
       ],
+      DummyValue.STRING,
     ],
   })
-  upsert(assetId: string, ocrDataList: Insertable<AssetOcrTable>[]) {
+  upsert(assetId: string, ocrDataList: Insertable<AssetOcrTable>[], searchText: string) {
     let query = this.db.with('deleted_ocr', (db) => db.deleteFrom('asset_ocr').where('assetId', '=', assetId));
     if (ocrDataList.length > 0) {
-      const searchText = ocrDataList.map((item) => item.text.trim()).join(' ');
       (query as any) = query
         .with('inserted_ocr', (db) => db.insertInto('asset_ocr').values(ocrDataList))
         .with('inserted_search', (db) =>

server/src/schema/migrations/1764483051488-OCRBigramsForCJK.ts

@@ -0,0 +1,24 @@
+import { Kysely, sql } from 'kysely';
+import { tokenizeForSearch } from 'src/utils/database';
+
+export async function up(db: Kysely<any>): Promise<void> {
+  await sql`truncate ${sql.table('ocr_search')}`.execute(db);
+  const batch = [];
+  for await (const { assetId, text } of db
+    .selectFrom('asset_ocr')
+    .select(['assetId', sql<string>`string_agg(text, ' ')`.as('text')])
+    .groupBy('assetId')
+    .stream()) {
+    batch.push({ assetId, text: tokenizeForSearch(text) });
+    if (batch.length >= 5000) {
+      await db.insertInto('ocr_search').values(batch).execute();
+      batch.length = 0;
+    }
+  }
+
+  if (batch.length > 0) {
+    await db.insertInto('ocr_search').values(batch).execute();
+  }
+}
+
+export async function down(): Promise<void> {}

server/src/services/ocr.service.spec.ts

@@ -12,8 +12,21 @@ describe(OcrService.name, () => {
     ({ sut, mocks } = newTestService(OcrService));
 
     mocks.config.getWorker.mockReturnValue(ImmichWorker.Microservices);
+    mocks.assetJob.getForOcr.mockResolvedValue({
+      visibility: AssetVisibility.Timeline,
+      previewFile: assetStub.image.files[1].path,
+    });
   });
 
+  const mockOcrResult = (...texts: string[]) => {
+    mocks.machineLearning.ocr.mockResolvedValue({
+      box: texts.flatMap((_, i) => Array.from({ length: 8 }, (_, j) => i * 10 + j)),
+      boxScore: texts.map(() => 0.9),
+      text: texts,
+      textScore: texts.map(() => 0.95),
+    });
+  };
+
   it('should work', () => {
     expect(sut).toBeDefined();
   });
@@ -72,10 +85,6 @@ describe(OcrService.name, () => {
         text: ['One Two Three', 'Four Five'],
         textScore: [0.95, 0.85],
       });
-      mocks.assetJob.getForOcr.mockResolvedValue({
-        visibility: AssetVisibility.Timeline,
-        previewFile: assetStub.image.files[1].path,
-      });
 
       expect(await sut.handleOcr({ id: assetStub.image.id })).toEqual(JobStatus.Success);
 
@@ -88,36 +97,40 @@ describe(OcrService.name, () => {
           maxResolution: 736,
         }),
       );
-      expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, [
-        {
-          assetId: assetStub.image.id,
-          boxScore: 0.9,
-          text: 'One Two Three',
-          textScore: 0.95,
-          x1: 10,
-          y1: 20,
-          x2: 30,
-          y2: 40,
-          x3: 50,
-          y3: 60,
-          x4: 70,
-          y4: 80,
-        },
-        {
-          assetId: assetStub.image.id,
-          boxScore: 0.8,
-          text: 'Four Five',
-          textScore: 0.85,
-          x1: 90,
-          y1: 100,
-          x2: 110,
-          y2: 120,
-          x3: 130,
-          y3: 140,
-          x4: 150,
-          y4: 160,
-        },
-      ]);
+      expect(mocks.ocr.upsert).toHaveBeenCalledWith(
+        assetStub.image.id,
+        [
+          {
+            assetId: assetStub.image.id,
+            boxScore: 0.9,
+            text: 'One Two Three',
+            textScore: 0.95,
+            x1: 10,
+            y1: 20,
+            x2: 30,
+            y2: 40,
+            x3: 50,
+            y3: 60,
+            x4: 70,
+            y4: 80,
+          },
+          {
+            assetId: assetStub.image.id,
+            boxScore: 0.8,
+            text: 'Four Five',
+            textScore: 0.85,
+            x1: 90,
+            y1: 100,
+            x2: 110,
+            y2: 120,
+            x3: 130,
+            y3: 140,
+            x4: 150,
+            y4: 160,
+          },
+        ],
+        'One Two Three Four Five',
+      );
     });
 
     it('should apply config settings', async () => {
@@ -133,11 +146,7 @@ describe(OcrService.name, () => {
           },
         },
       });
-      mocks.machineLearning.ocr.mockResolvedValue({ box: [], boxScore: [], text: [], textScore: [] });
-      mocks.assetJob.getForOcr.mockResolvedValue({
-        visibility: AssetVisibility.Timeline,
-        previewFile: assetStub.image.files[1].path,
-      });
+      mockOcrResult();
 
       expect(await sut.handleOcr({ id: assetStub.image.id })).toEqual(JobStatus.Success);
 
@@ -150,7 +159,7 @@ describe(OcrService.name, () => {
           maxResolution: 1500,
         }),
       );
-      expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, []);
+      expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, [], '');
     });
 
     it('should skip invisible assets', async () => {
@@ -173,5 +182,83 @@ describe(OcrService.name, () => {
       expect(mocks.machineLearning.ocr).not.toHaveBeenCalled();
       expect(mocks.ocr.upsert).not.toHaveBeenCalled();
     });
+
+    describe('search tokenization', () => {
+      it('should generate bigrams for Chinese text', async () => {
+        mockOcrResult('機器學習');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), '機器 器學 學習');
+      });
+
+      it('should generate bigrams for Japanese text', async () => {
+        mockOcrResult('テスト');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), 'テス スト');
+      });
+
+      it('should generate bigrams for Korean text', async () => {
+        mockOcrResult('한국어');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), '한국 국어');
+      });
+
+      it('should pass through Latin text unchanged', async () => {
+        mockOcrResult('Hello World');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), 'Hello World');
+      });
+
+      it('should handle mixed CJK and Latin text', async () => {
+        mockOcrResult('機器學習Model');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), '機器 器學 學習 Model');
+      });
+
+      it('should handle year followed by CJK', async () => {
+        mockOcrResult('2024年レポート');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(
+          assetStub.image.id,
+          expect.any(Array),
+          '2024 年レ レポ ポー ート',
+        );
+      });
+
+      it('should join multiple OCR boxes', async () => {
+        mockOcrResult('機器', 'Learning');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), '機器 Learning');
+      });
+
+      it('should normalize whitespace', async () => {
+        mockOcrResult('  Hello   World  ');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), 'Hello World');
+      });
+
+      it('should keep single CJK characters', async () => {
+        mockOcrResult('A', '中', 'B');
+
+        await sut.handleOcr({ id: assetStub.image.id });
+
+        expect(mocks.ocr.upsert).toHaveBeenCalledWith(assetStub.image.id, expect.any(Array), 'A 中 B');
+      });
+    });
   });
 });

server/src/services/ocr.service.ts

@@ -5,6 +5,7 @@ import { AssetVisibility, JobName, JobStatus, QueueName } from 'src/enum';
 import { OCR } from 'src/repositories/machine-learning.repository';
 import { BaseService } from 'src/services/base.service';
 import { JobItem, JobOf } from 'src/types';
+import { tokenizeForSearch } from 'src/utils/database';
 import { isOcrEnabled } from 'src/utils/misc';
 
 @Injectable()
@@ -53,8 +54,8 @@ export class OcrService extends BaseService {
     }
 
     const ocrResults = await this.machineLearningRepository.ocr(asset.previewFile, machineLearning.ocr);
-
-    await this.ocrRepository.upsert(id, this.parseOcrResults(id, ocrResults));
+    const { ocrDataList, searchText } = this.parseOcrResults(id, ocrResults);
+    await this.ocrRepository.upsert(id, ocrDataList, searchText);
 
     await this.assetRepository.upsertJobStatus({ assetId: id, ocrAt: new Date() });
 
@@ -64,7 +65,9 @@ export class OcrService extends BaseService {
 
   private parseOcrResults(id: string, { box, boxScore, text, textScore }: OCR) {
     const ocrDataList = [];
+    const searchTokens = [];
     for (let i = 0; i < text.length; i++) {
+      const rawText = text[i];
       const boxOffset = i * 8;
       ocrDataList.push({
         assetId: id,
@@ -78,9 +81,11 @@ export class OcrService extends BaseService {
         y4: box[boxOffset + 7],
         boxScore: boxScore[i],
         textScore: textScore[i],
-        text: text[i],
+        text: rawText,
       });
+      searchTokens.push(...tokenizeForSearch(rawText));
     }
-    return ocrDataList;
+
+    return { ocrDataList, searchText: searchTokens.join(' ') };
   }
 }

server/src/utils/database.ts

@@ -306,6 +306,46 @@ export function withTagId<O>(qb: SelectQueryBuilder<DB, 'asset', O>, tagId: stri
   );
 }
 
+const isCJK = (c: number): boolean =>
+  (c >= 0x4e_00 && c <= 0x9f_ff) ||
+  (c >= 0xac_00 && c <= 0xd7_af) ||
+  (c >= 0x30_40 && c <= 0x30_9f) ||
+  (c >= 0x30_a0 && c <= 0x30_ff) ||
+  (c >= 0x34_00 && c <= 0x4d_bf);
+
+export const tokenizeForSearch = (text: string): string[] => {
+  /* eslint-disable unicorn/prefer-code-point */
+  const tokens: string[] = [];
+  let i = 0;
+  while (i < text.length) {
+    const c = text.charCodeAt(i);
+    if (c <= 32) {
+      i++;
+      continue;
+    }
+
+    const start = i;
+    if (isCJK(c)) {
+      while (i < text.length && isCJK(text.charCodeAt(i))) {
+        i++;
+      }
+      if (i - start === 1) {
+        tokens.push(text[start]);
+      } else {
+        for (let k = start; k < i - 1; k++) {
+          tokens.push(text[k] + text[k + 1]);
+        }
+      }
+    } else {
+      while (i < text.length && text.charCodeAt(i) > 32 && !isCJK(text.charCodeAt(i))) {
+        i++;
+      }
+      tokens.push(text.slice(start, i));
+    }
+  }
+  return tokens;
+};
+
 const joinDeduplicationPlugin = new DeduplicateJoinsPlugin();
 /** TODO: This should only be used for search-related queries, not as a general purpose query builder */
 
@@ -391,7 +431,7 @@ export function searchAssetBuilder(kysely: Kysely<DB>, options: AssetSearchBuild
     .$if(!!options.ocr, (qb) =>
       qb
         .innerJoin('ocr_search', 'asset.id', 'ocr_search.assetId')
-        .where(() => sql`f_unaccent(ocr_search.text) %>> f_unaccent(${options.ocr!})`),
+        .where(() => sql`f_unaccent(ocr_search.text) %>> f_unaccent(${tokenizeForSearch(options.ocr!).join(' ')})`),
     )
     .$if(!!options.type, (qb) => qb.where('asset.type', '=', options.type!))
     .$if(options.isFavorite !== undefined, (qb) => qb.where('asset.isFavorite', '=', options.isFavorite!))