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API Pengenalan Teks ML Kit v2 dapat mengenali teks dalam himpunan karakter China, Devanagari,
Jepang, Korea, dan Latin. API ini juga dapat digunakan untuk mengotomatiskan
tugas entri data seperti memproses kartu kredit, tanda terima, dan kartu nama.
Mengenali teks di berbagai skrip dan bahasa Mendukung pengenalan
teks dalam skrip China, Devanagari, Jepang, Korea, dan Latin
Menganalisis struktur teks Mendukung deteksi simbol, elemen,
garis, dan paragraf
Identifikasi bahasa teks Mengidentifikasi bahasa teks yang dikenali
Pengenalan real-time Dapat mengenali teks secara real time di berbagai
perangkat
Struktur teks
Pengenal Teks memilah teks menjadi blok, garis, elemen, dan simbol.
Secara garis besar:
Blok adalah rangkaian baris teks yang berdekatan, seperti paragraf atau
kolom,
Garis adalah rangkaian kata yang berdekatan pada sumbu yang sama, dan
Elemen adalah kumpulan karakter alfanumerik ("kata") yang berdekatan pada sumbu yang sama dalam sebagian besar bahasa Latin, atau kata dalam bahasa lainnya
Simbol adalah karakter alfanumerik tunggal pada sumbu yang sama dalam kebanyakan bahasa Latin, atau karakter dalam bahasa lain
Gambar di bawah menyoroti contoh masing-masing dalam urutan menurun. Blok
yang pertama kali ditandai, dalam warna sian, adalah Blok teks. Kumpulan kedua
blok yang disorot, dengan warna biru, adalah Baris teks. Terakhir, kumpulan ketiga blok yang disorot, dengan warna biru tua, adalah Words.
Untuk semua blok, garis, elemen, dan simbol yang terdeteksi, API akan menampilkan
kotak pembatas, titik sudut, informasi rotasi, skor keyakinan,
bahasa yang dikenali, dan teks yang dikenali.
[null,null,["Terakhir diperbarui pada 2025-07-25 UTC."],[[["\u003cp\u003eThe ML Kit Text Recognition v2 API recognizes text in Chinese, Devanagari, Japanese, Korean, and Latin scripts and can automate data entry for documents like credit cards and receipts.\u003c/p\u003e\n"],["\u003cp\u003eIt analyzes text structure by identifying blocks, lines, elements (words), and symbols, returning bounding boxes, corner points, and confidence scores for each.\u003c/p\u003e\n"],["\u003cp\u003eThe API supports real-time text recognition on various devices and can identify the language of the recognized text.\u003c/p\u003e\n"]]],["The ML Kit Text Recognition v2 API recognizes text in Chinese, Devanagari, Japanese, Korean, and Latin scripts. It analyzes text structure by detecting blocks, lines, elements, and symbols, and identifies the language. The API provides bounding boxes, corner points, rotation, confidence scores, recognized languages, and text for detected text. This API can be used for automating data entry from credit cards, receipts and business cards. It also support real time text recognition.\n"],null,["# Text recognition v2\n\nThe ML Kit Text Recognition v2 API can recognize text in any Chinese, Devanagari,\nJapanese, Korean and Latin character set. The API can also be used to automate\ndata-entry tasks such as processing credit cards, receipts, and business cards.\n\n[iOS](/ml-kit/vision/text-recognition/v2/ios)\n[Android](/ml-kit/vision/text-recognition/v2/android)\n\nKey capabilities\n----------------\n\n- **Recognize text across various scripts and languages** Supports recognizing text in Chinese, Devanagari, Japanese, Korean and Latin scripts\n- **Analyzes structure of text** Supports detection of symbols, elements, lines and paragraphs\n- **Identify language of text** Identifies the language of the recognized text\n- **Real-time recognition** Can recognize text in real-time on a wide range of devices\n\nText structure\n--------------\n\nThe Text Recognizer segments text into blocks, lines, elements and symbols.\nRoughly speaking:\n\n- a **Block** is a contiguous set of text lines, such as a paragraph or\n column,\n\n- a **Line** is a contiguous set of words on the same axis, and\n\n- an **Element** is a contiguous set of alphanumeric characters (\"word\") on the\n same axis in most Latin languages, or a word in others\n\n- an **Symbol** is a single alphanumeric character on the\n same axis in most Latin languages, or a character in others\n\nThe image below highlights examples of each of these in descending order. The\nfirst highlighted block, in cyan, is a Block of text. The second set of\nhighlighted blocks, in blue, are Lines of text. Finally, the third set of\nhighlighted blocks, in dark blue, are Words.\n\nFor all detected blocks, lines, elements and symbols, the API returns the\nbounding boxes, corner points, rotation information, confidence score,\nrecognized languages and recognized text.\n\nExample results\n---------------\n\n\u003cbr /\u003e\n\nPhoto: [Dietmar Rabich](//commons.wikimedia.org/wiki/User:XRay \"User:XRay\"), [Wikimedia Commons](//commons.wikimedia.org/wiki/Main_Page \"Main Page\"), [\"Düsseldorf,\nWege der parlamentarischen Demokratie -- 2015 -- 8123\"](//commons.wikimedia.org/wiki/File:D%C3%BCsseldorf,_Wege_der_parlamentarischen_Demokratie_--_2015_--_8123.jpg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)\n\n| Recognized Text ||\n|--------|---------------------------------------|\n| Text | Wege der parlamentarischen Demokratie |\n| Blocks | (1 block) |\n\n| Block 0 ||\n|--------------------------|------------------------------------------------|\n| Text | Wege der parlamentarischen Demokratie |\n| Frame | (296, 665 - 796, 882) |\n| Corner Points | (296, 719), (778, 665), (796, 828), (314, 882) |\n| Recognized Language Code | de |\n| Lines | (3 lines) |\n\n| Line 0 ||\n|--------------------------|------------------------------------------------|\n| Text | Wege der |\n| Frame | (434, 678 - 670, 749) |\n| Corner Points | (434, 705), (665, 678), (670, 722), (439, 749) |\n| Recognized Language Code | de |\n| Confidence Score | 0.8766741 |\n| Rotation Degree | -6.6116457 |\n| Elements | (2 elements) |\n\n| Element 0 ||\n|--------------------------|------------------------------------------------|\n| Text | Wege |\n| Frame | (434, 689 - 575, 749) |\n| Corner Points | (434, 705), (570, 689), (575, 733), (439, 749) |\n| Recognized Language Code | de |\n| Confidence Score | 0.8964844 |\n| Rotation Degree | -6.6116457 |\n| Elements | (4 elements) |\n\n| Symbol 0 ||\n|------------------|------------------------------------------------|\n| Text | W |\n| Frame | (434, 698 - 500, 749) |\n| Corner Points | (434, 706), (495, 698), (500, 741), (439, 749) |\n| Confidence Score | 0.87109375 |\n| Rotation Degree | -6.611646 |"]]
